The proposed method uses random forests with steerable features to automatically localize fetal organs (heart, lungs, liver) in MRI. During training, images are mapped to a standard coordinate system defined by anatomical landmarks and normalized for fetal age. At testing, features are extracted in rotating coordinate systems to account for the fetus' unpredictable orientation. The method was tested on healthy fetuses and fetuses with IUGR, achieving over 90% detection rates for healthy fetuses without motion artifacts, and 83%, 78%, 67% detection rates for heart, lungs, liver respectively in the presence of motion. The method can initialize segmentation and motion correction and automatically orient volumes based on fetal anatomy.
3D Position Tracking System for Flexible CystoscopyCSCJournals
Flexible cystoscopy is an examination that allows physicians to look inside the bladder. In flexible cystoscopy, beginner physicians tend to lose track of the observation due to complex handling patterns of a flexible cystoscope and poor characteristics of the bladder. In this paper, as a diagnostic support tool for beginner physicians in flexible cystoscopy, we propose a system for tracking the observation using cystoscopic images. Our system discriminates three handling patterns of a flexible cystoscope, namely bending, rotation, or insertion. To discriminate the handling patterns accurately, we propose to use the degree of bending, rotation, or insertion as features for the discrimination as well as ZNCC-based optical flows. These features are learned by a Random Forest classifier. The classifier discriminates sequential handling patterns of the cystoscope by a time-series analysis. Experimental results on ten videos obtained in flexible cystoscopy show that each of the three handling patterns were correctly discriminated over 90% in average. In addition, we reproduced the observation in a virtual bladder we propose.
MEDICAL IMAGE PROCESSING METHODOLOGY FOR LIVER TUMOUR DIAGNOSISijsc
Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver
segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples
collected from ten patients and received 90% accuracy rate.
ENHANCING SEGMENTATION APPROACHES FROM GAUSSIAN MIXTURE MODEL AND EXPECTED MA...Christo Ananth
Automatic liver tumor segmentation would bigly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)Kevin Keraudren
Localization of the fetal brain in MRI poses challenges due to fetal motion during image acquisition. The authors propose a 2D detection method using Maximally Stable Extremal Regions and bundled SIFT features to classify regions as brain or non-brain. A RANSAC procedure then fits an axis-aligned 3D box to the detected regions. On a dataset of 59 fetuses, the method obtained a median error of 5.7mm from ground truth with no missed detections, outperforming alternatives using 2D or 3D SIFT features. The prior knowledge of fetal brain size based on gestational age improves robustness to motion artifacts.
This document discusses image reconstruction techniques for detecting and segmenting tumor cells in brain images. It begins with an introduction to image reconstruction and its applications in medicine. The existing methods for brain tumor detection using MRI are reviewed. The proposed method involves preprocessing MRI images, enhancing contrast, dividing images into quadrants, analyzing pixel intensities and entropy to identify the quadrant most likely containing a tumor, applying thresholding and segmentation techniques like watershed to that quadrant to identify and mark the tumor boundaries, and analyzing features of detected tumors. Results are shown of tumor detection and segmentation using watershed segmentation, morphological operators, and feature identification to classify tumors as benign or malignant. The conclusion is that the proposed method provides faster and more targeted tumor detection compared to existing
Liver extraction using histogram and morphologyeSAT Journals
Abstract
Liver is the largest glandular organ important for survival in human body. Computed tomography is generally used to image liver
due to its precision. This paper presents a method to extract liver from computed tomography (CT) abdomen images in axial
orientation. A traditional segmentation method based on histogram and morphology is proposed herein. Histogram is used to
analyze the intensity distribution, morphological operations are used to disconnect liver from the neighboring organs and greatest
connected pixels are extracted. The experimental results of the proposed method when applied to CT abdomen images with
contrast are presented and the effectiveness is discussed in accordance to the manual tracing obtained from the radiologist. Dice
similarity co-efficient amounts to 94% in the proposed method.
Keywords: Segmentation, Extraction, Histogram, Morphology, Connected Component, CT Liver
Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Reh...Luca Parisi
Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
Comparison of Three Segmentation Methods for Breast Ultrasound Images Based o...IJECEIAES
Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (CV) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.
3D Position Tracking System for Flexible CystoscopyCSCJournals
Flexible cystoscopy is an examination that allows physicians to look inside the bladder. In flexible cystoscopy, beginner physicians tend to lose track of the observation due to complex handling patterns of a flexible cystoscope and poor characteristics of the bladder. In this paper, as a diagnostic support tool for beginner physicians in flexible cystoscopy, we propose a system for tracking the observation using cystoscopic images. Our system discriminates three handling patterns of a flexible cystoscope, namely bending, rotation, or insertion. To discriminate the handling patterns accurately, we propose to use the degree of bending, rotation, or insertion as features for the discrimination as well as ZNCC-based optical flows. These features are learned by a Random Forest classifier. The classifier discriminates sequential handling patterns of the cystoscope by a time-series analysis. Experimental results on ten videos obtained in flexible cystoscopy show that each of the three handling patterns were correctly discriminated over 90% in average. In addition, we reproduced the observation in a virtual bladder we propose.
MEDICAL IMAGE PROCESSING METHODOLOGY FOR LIVER TUMOUR DIAGNOSISijsc
Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver
segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples
collected from ten patients and received 90% accuracy rate.
ENHANCING SEGMENTATION APPROACHES FROM GAUSSIAN MIXTURE MODEL AND EXPECTED MA...Christo Ananth
Automatic liver tumor segmentation would bigly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)Kevin Keraudren
Localization of the fetal brain in MRI poses challenges due to fetal motion during image acquisition. The authors propose a 2D detection method using Maximally Stable Extremal Regions and bundled SIFT features to classify regions as brain or non-brain. A RANSAC procedure then fits an axis-aligned 3D box to the detected regions. On a dataset of 59 fetuses, the method obtained a median error of 5.7mm from ground truth with no missed detections, outperforming alternatives using 2D or 3D SIFT features. The prior knowledge of fetal brain size based on gestational age improves robustness to motion artifacts.
This document discusses image reconstruction techniques for detecting and segmenting tumor cells in brain images. It begins with an introduction to image reconstruction and its applications in medicine. The existing methods for brain tumor detection using MRI are reviewed. The proposed method involves preprocessing MRI images, enhancing contrast, dividing images into quadrants, analyzing pixel intensities and entropy to identify the quadrant most likely containing a tumor, applying thresholding and segmentation techniques like watershed to that quadrant to identify and mark the tumor boundaries, and analyzing features of detected tumors. Results are shown of tumor detection and segmentation using watershed segmentation, morphological operators, and feature identification to classify tumors as benign or malignant. The conclusion is that the proposed method provides faster and more targeted tumor detection compared to existing
Liver extraction using histogram and morphologyeSAT Journals
Abstract
Liver is the largest glandular organ important for survival in human body. Computed tomography is generally used to image liver
due to its precision. This paper presents a method to extract liver from computed tomography (CT) abdomen images in axial
orientation. A traditional segmentation method based on histogram and morphology is proposed herein. Histogram is used to
analyze the intensity distribution, morphological operations are used to disconnect liver from the neighboring organs and greatest
connected pixels are extracted. The experimental results of the proposed method when applied to CT abdomen images with
contrast are presented and the effectiveness is discussed in accordance to the manual tracing obtained from the radiologist. Dice
similarity co-efficient amounts to 94% in the proposed method.
Keywords: Segmentation, Extraction, Histogram, Morphology, Connected Component, CT Liver
Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Reh...Luca Parisi
Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
Comparison of Three Segmentation Methods for Breast Ultrasound Images Based o...IJECEIAES
Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (CV) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.
A theoretical study on partially automated method for (prostate) cancer pinpo...eSAT Journals
Abstract A Partially Automated method for (Prostate) Cancer pinpoint using Multi-parametric magnetic resonance imaging has been proposed in this paper, which can be used in guiding surgery. A Random Walker (RW) algorithm has been analyzed with seed initialization to perform (Prostate) cancer pinpoint using Magnetic Resonance Imaging (MRI). Segmentation can be done by using Random Walker (RW) algorithm which has to be considered to be a fastest method. Random Walker (RW) method can be used with multi-parametric magnetic resonance imaging (MRI) and then by using Support Vector Machine (SVM) method, we can determine the seed points in a partially automated manner. By using this method, more weights to the image can be assigned in order to produce improved segmentation process. The proposed method can also give high specificity rate without reducing the sensitivity which is better than earlier methods and fisher sign test can be also used to find the statistical differences. Index terms:Support Vector Machine, Random Walker, Magnetic Prediction, Magnetic Resonance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document describes a study that aims to compare two methods of measuring muscle volume: MRI and ultrasound combined with 3D motion capture. Five female subjects participated in MRI scans and ultrasound imaging of their right triceps surae muscle while motion capture data was recorded. The study hopes to calculate muscle volumes from ultrasound images using a transformation matrix applied to outline points, and to show these volumes match those calculated from MRI images, validating ultrasound as an acceptable alternative method. The document outlines the data collection methods, which involved capturing ultrasound images and motion data at marked points on the leg. It also provides preliminary results and plans for ongoing MATLAB processing of the images to determine muscle volumes.
Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Imagesijfls
The document summarizes a study that developed an automated method for segmenting vertebrae from T1-weighted spinal MRI images using fuzzy c-means clustering. The method first performs anisotropic diffusion filtering for pre-processing. It then applies fuzzy c-means clustering to segment the vertebrae. Various morphological operations are used as post-processing to extract and label the individual vertebrae. The method is compared to Otsu thresholding and K-means clustering. Validation using the Dice coefficient and Hausdorff distance measures showed the fuzzy clustering method provided more accurate segmentation of the vertebrae from the MR images.
This paper presents a novel edge-based method for segmenting masses in mammogram images. The method first enhances the image contrast and reduces noise. It then computes a texture descriptor for each pixel to create an energy texture image. Edge detection is performed on this image to identify closed-path edges representing possible mass regions. The boundary of the identified mass region is used as the segmented contour of the mass. Preliminary results show the proposed method outlines masses more closely than radiologist markings.
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
IMAGINARY ANATOMY LINE MEASUREMENTS ON THE THORAXAM Publications
An imaginary line measurement of the thorax is important for clinical need. There are several imaginary anatomical lines on the thorax that can be measured directly using a tape meter, such as: midclavicular line, midaxilla line, chest width, chest circumference, distance of incisura jugularis sterni to the processusxiphoideus, intermidclavicular line, the upper thoracic dome and the lower thoracic dome. The objective of this article is to provide information on the average anatomical imaginary line value on the thoracic of the adult male. The data was then used for manufacturing of a thoracic anatomical phantom. Some marks of imaginary lines formed by clavicula, costae, and sternum were used as a reference in measurement. The anatomical imaginary line measurements were performed using a tape meter directly on 10 adult male samples, and carried out based on Chest X ray image of the same samples on a Computer Radiography (CR) workstation computer. Both measurements were then analyzed; ultrasonography examination was used to get soft tissue thickness for correction. Average values for midclavicular line variable of measurement using a tape measure was (29.10 ± 1.80) cm and the average value for the midclavicular line variable of measurement using Chest X ray on a CR workstation computer was (21.24 ± 0.73) cm. The average values for the variables intermidclavicular line measurement using a tape measure was (19.60 ± 0.69) cm and the average value for the variable intermidclavicular line of measurement using Chest X ray on a CR workstation computer was (16.53 ± 1.19) cm.
This paper presents an automated method for extracting regional data from positron emission tomography (PET) images. The method uses magnetic resonance (MR) images co-registered to PET images to individualize a standard set of regions of interest (ROIs). The ROIs are refined based on each individual's MR image to account for anatomical variations. Time-activity curves obtained with the automated method are highly correlated with those obtained manually, with differences generally within 8%. The automated method eliminates variability between raters, reduces analysis time, and provides accurate PET data quantification as an alternative to manual delineation of ROIs.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
A Numerical Investigation of Breast Compression: Lesion ModelingWaqas Tariq
Researchers have developed finite element (FE) models from preoperative medical images to simulate intraoperative breast compression. Applications for these FE models include mammography, non-rigid image registration, and MRI guided biopsy. Efficient FE breast models have been constructed that model suspect lesions as a single element or node within the FE breast mesh. At the expense of efficiency, other researchers have modeled the actual lesion geometry within the FE breast mesh (conformal breast-lesion mesh). Modeling the actual lesion geometry provides lesion boundary spatial information, which is lost in FE breast models that model suspect lesions as a single element or node within the FE breast mesh. In this paper, we used a commercial finite element analysis (FEA) program to construct a biomechanical breast model from patient specific MR volumes. A laterally situated lesion was identified in the diagnostic MRI. We used the FE model to simulate breast compression during an MRI guided biopsy. Our objective was to investigate the efficacy of independently discretizing the breast and lesion geometries and using a kinematic constraint to associate the lesion nodes to the nodes in the breast mesh based on their isoparametric position. This study showed that it is possible to construct an accurate and efficient FE breast model that considers the actual lesion geometry. With 61 mm of breast compression, the lesion centroid was localized to within 3.8 mm of its actual position. As compared to a conformal breast-lesion FE mesh, the element count was also reduced by 53%. These findings suggest that it is possible to predict the position of a suspect lesion\'s centroid and boundary within clinical time constraints (< 30 minutes).
Automated Detection of Optic Disc in Retinal FundusImages Using PCAiosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Feature Extraction and Analysis on Xinjiang High Morbidity of Kazak Esophagea...CSCJournals
Image feature extraction technology has been widely applied in image data mining, pattern recognition and classification. Esophageal cancer is a common digestive malignant tumor, China is one of the world's highest incidence and mortality rates of esophageal cancer among the countries, Xinjiang Uygur Autonomous Region is a high incidence area of esophageal cancer, and the kazak is the esophageal cancer high-risk groups. In this paper, we selected 60 advanced esophageal X-ray barium images, half of them are constricted esophagus and the rest are ulcerous esophagus. Firstly, image preprocessing approaches were used to preprocess images. Secondly, extracting the gray-scale histogram features and the GLCM features of the images, then composing the two features into comprehensive feature. Finally, using Bayes discriminant analysis to verify the classification ability of the comprehensive feature. The classification accuracy for constricted esophagus was 86.7%, for ulcerous esophagus was 93.3%.
This document discusses the use of artificial intelligence in breast imaging, specifically for the early detection of breast cancer. It provides background on common breast imaging techniques like mammography, tomosynthesis, ultrasound and MRI. It then discusses traditional CAD (computer-aided detection) systems and their limitations in detecting cancers. The document introduces artificial intelligence and how techniques like machine learning and deep learning can improve upon traditional CAD systems. It reviews several studies that have found AI-based systems can help radiologists achieve higher accuracy and reduce false-positive rates compared to unaided diagnosis. Finally, it mentions several companies developing AI solutions for applications in mammography, tomosynthesis and breast MRI.
Optimization Based Liver Contour Extraction of Abdominal CT Images IJECEIAES
This paper introduces computer aided analysis system for diagnosis of liver abnormality in abdominal CT images. Segmenting the liver and visualizing the region of interest is a most challenging task in the field of cancer imaging, due to small observable changes between healthy and unhealthy liver. In this paper, hybrid approach for automatic extraction of liver contour is proposed. To obtain optimal threshold, the proposed work integrates segmentation method with optimization technique in order to provide better accuracy. This method uses bilateral filter for preprocessing and Fuzzy C means clustering (FCM) for segmentation. Mean Grey Wolf Optimization technique (mGWO) has been used to get the optimal threshold. This threshold is used for segmenting the region of interest. From the segmented output, largest connected region is identified using Label Connected Component (LCC) algorithm. The effectiveness of proposed method is quantitatively evaluated by comparing with ground truth obtained from radiologists. The performance criteria like dice coefficient, true positive error and misclassification rate are taken for evaluation.
Este documento define la planificación y describe sus elementos, orígenes, tipos y fases. La planificación es un proceso sistemático para lograr objetivos de manera eficiente mediante la definición de metas, actividades y recursos. Tiene como propósitos proteger a la organización y elevar su éxito. La planificación surgió inicialmente en la Unión Soviética y Francia en los siglos XVI-XVIII y se desarrolló en el siglo XX. Incluye planificación estratégica, táctica y operativa en distintos
Docencia, investigación, gestión y extensiónSamygogo
La educación ha evolucionado de un modelo tradicional de enseñanza a uno donde el docente guía el proceso educativo y acompaña al estudiante en un ambiente de valores, cultura e innovación orientado al crecimiento personal y social. Para lograr una articulación efectiva entre los programas y objetivos de las universidades, todos los involucrados deben comprometerse con estrategias que cumplan la visión de mejora continua e innovación para satisfacer las necesidades de la sociedad de manera global. Algunas estrategias que pueden desarrollar el espíritu
A theoretical study on partially automated method for (prostate) cancer pinpo...eSAT Journals
Abstract A Partially Automated method for (Prostate) Cancer pinpoint using Multi-parametric magnetic resonance imaging has been proposed in this paper, which can be used in guiding surgery. A Random Walker (RW) algorithm has been analyzed with seed initialization to perform (Prostate) cancer pinpoint using Magnetic Resonance Imaging (MRI). Segmentation can be done by using Random Walker (RW) algorithm which has to be considered to be a fastest method. Random Walker (RW) method can be used with multi-parametric magnetic resonance imaging (MRI) and then by using Support Vector Machine (SVM) method, we can determine the seed points in a partially automated manner. By using this method, more weights to the image can be assigned in order to produce improved segmentation process. The proposed method can also give high specificity rate without reducing the sensitivity which is better than earlier methods and fisher sign test can be also used to find the statistical differences. Index terms:Support Vector Machine, Random Walker, Magnetic Prediction, Magnetic Resonance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document describes a study that aims to compare two methods of measuring muscle volume: MRI and ultrasound combined with 3D motion capture. Five female subjects participated in MRI scans and ultrasound imaging of their right triceps surae muscle while motion capture data was recorded. The study hopes to calculate muscle volumes from ultrasound images using a transformation matrix applied to outline points, and to show these volumes match those calculated from MRI images, validating ultrasound as an acceptable alternative method. The document outlines the data collection methods, which involved capturing ultrasound images and motion data at marked points on the leg. It also provides preliminary results and plans for ongoing MATLAB processing of the images to determine muscle volumes.
Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Imagesijfls
The document summarizes a study that developed an automated method for segmenting vertebrae from T1-weighted spinal MRI images using fuzzy c-means clustering. The method first performs anisotropic diffusion filtering for pre-processing. It then applies fuzzy c-means clustering to segment the vertebrae. Various morphological operations are used as post-processing to extract and label the individual vertebrae. The method is compared to Otsu thresholding and K-means clustering. Validation using the Dice coefficient and Hausdorff distance measures showed the fuzzy clustering method provided more accurate segmentation of the vertebrae from the MR images.
This paper presents a novel edge-based method for segmenting masses in mammogram images. The method first enhances the image contrast and reduces noise. It then computes a texture descriptor for each pixel to create an energy texture image. Edge detection is performed on this image to identify closed-path edges representing possible mass regions. The boundary of the identified mass region is used as the segmented contour of the mass. Preliminary results show the proposed method outlines masses more closely than radiologist markings.
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
IMAGINARY ANATOMY LINE MEASUREMENTS ON THE THORAXAM Publications
An imaginary line measurement of the thorax is important for clinical need. There are several imaginary anatomical lines on the thorax that can be measured directly using a tape meter, such as: midclavicular line, midaxilla line, chest width, chest circumference, distance of incisura jugularis sterni to the processusxiphoideus, intermidclavicular line, the upper thoracic dome and the lower thoracic dome. The objective of this article is to provide information on the average anatomical imaginary line value on the thoracic of the adult male. The data was then used for manufacturing of a thoracic anatomical phantom. Some marks of imaginary lines formed by clavicula, costae, and sternum were used as a reference in measurement. The anatomical imaginary line measurements were performed using a tape meter directly on 10 adult male samples, and carried out based on Chest X ray image of the same samples on a Computer Radiography (CR) workstation computer. Both measurements were then analyzed; ultrasonography examination was used to get soft tissue thickness for correction. Average values for midclavicular line variable of measurement using a tape measure was (29.10 ± 1.80) cm and the average value for the midclavicular line variable of measurement using Chest X ray on a CR workstation computer was (21.24 ± 0.73) cm. The average values for the variables intermidclavicular line measurement using a tape measure was (19.60 ± 0.69) cm and the average value for the variable intermidclavicular line of measurement using Chest X ray on a CR workstation computer was (16.53 ± 1.19) cm.
This paper presents an automated method for extracting regional data from positron emission tomography (PET) images. The method uses magnetic resonance (MR) images co-registered to PET images to individualize a standard set of regions of interest (ROIs). The ROIs are refined based on each individual's MR image to account for anatomical variations. Time-activity curves obtained with the automated method are highly correlated with those obtained manually, with differences generally within 8%. The automated method eliminates variability between raters, reduces analysis time, and provides accurate PET data quantification as an alternative to manual delineation of ROIs.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
A Numerical Investigation of Breast Compression: Lesion ModelingWaqas Tariq
Researchers have developed finite element (FE) models from preoperative medical images to simulate intraoperative breast compression. Applications for these FE models include mammography, non-rigid image registration, and MRI guided biopsy. Efficient FE breast models have been constructed that model suspect lesions as a single element or node within the FE breast mesh. At the expense of efficiency, other researchers have modeled the actual lesion geometry within the FE breast mesh (conformal breast-lesion mesh). Modeling the actual lesion geometry provides lesion boundary spatial information, which is lost in FE breast models that model suspect lesions as a single element or node within the FE breast mesh. In this paper, we used a commercial finite element analysis (FEA) program to construct a biomechanical breast model from patient specific MR volumes. A laterally situated lesion was identified in the diagnostic MRI. We used the FE model to simulate breast compression during an MRI guided biopsy. Our objective was to investigate the efficacy of independently discretizing the breast and lesion geometries and using a kinematic constraint to associate the lesion nodes to the nodes in the breast mesh based on their isoparametric position. This study showed that it is possible to construct an accurate and efficient FE breast model that considers the actual lesion geometry. With 61 mm of breast compression, the lesion centroid was localized to within 3.8 mm of its actual position. As compared to a conformal breast-lesion FE mesh, the element count was also reduced by 53%. These findings suggest that it is possible to predict the position of a suspect lesion\'s centroid and boundary within clinical time constraints (< 30 minutes).
Automated Detection of Optic Disc in Retinal FundusImages Using PCAiosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Feature Extraction and Analysis on Xinjiang High Morbidity of Kazak Esophagea...CSCJournals
Image feature extraction technology has been widely applied in image data mining, pattern recognition and classification. Esophageal cancer is a common digestive malignant tumor, China is one of the world's highest incidence and mortality rates of esophageal cancer among the countries, Xinjiang Uygur Autonomous Region is a high incidence area of esophageal cancer, and the kazak is the esophageal cancer high-risk groups. In this paper, we selected 60 advanced esophageal X-ray barium images, half of them are constricted esophagus and the rest are ulcerous esophagus. Firstly, image preprocessing approaches were used to preprocess images. Secondly, extracting the gray-scale histogram features and the GLCM features of the images, then composing the two features into comprehensive feature. Finally, using Bayes discriminant analysis to verify the classification ability of the comprehensive feature. The classification accuracy for constricted esophagus was 86.7%, for ulcerous esophagus was 93.3%.
This document discusses the use of artificial intelligence in breast imaging, specifically for the early detection of breast cancer. It provides background on common breast imaging techniques like mammography, tomosynthesis, ultrasound and MRI. It then discusses traditional CAD (computer-aided detection) systems and their limitations in detecting cancers. The document introduces artificial intelligence and how techniques like machine learning and deep learning can improve upon traditional CAD systems. It reviews several studies that have found AI-based systems can help radiologists achieve higher accuracy and reduce false-positive rates compared to unaided diagnosis. Finally, it mentions several companies developing AI solutions for applications in mammography, tomosynthesis and breast MRI.
Optimization Based Liver Contour Extraction of Abdominal CT Images IJECEIAES
This paper introduces computer aided analysis system for diagnosis of liver abnormality in abdominal CT images. Segmenting the liver and visualizing the region of interest is a most challenging task in the field of cancer imaging, due to small observable changes between healthy and unhealthy liver. In this paper, hybrid approach for automatic extraction of liver contour is proposed. To obtain optimal threshold, the proposed work integrates segmentation method with optimization technique in order to provide better accuracy. This method uses bilateral filter for preprocessing and Fuzzy C means clustering (FCM) for segmentation. Mean Grey Wolf Optimization technique (mGWO) has been used to get the optimal threshold. This threshold is used for segmenting the region of interest. From the segmented output, largest connected region is identified using Label Connected Component (LCC) algorithm. The effectiveness of proposed method is quantitatively evaluated by comparing with ground truth obtained from radiologists. The performance criteria like dice coefficient, true positive error and misclassification rate are taken for evaluation.
Este documento define la planificación y describe sus elementos, orígenes, tipos y fases. La planificación es un proceso sistemático para lograr objetivos de manera eficiente mediante la definición de metas, actividades y recursos. Tiene como propósitos proteger a la organización y elevar su éxito. La planificación surgió inicialmente en la Unión Soviética y Francia en los siglos XVI-XVIII y se desarrolló en el siglo XX. Incluye planificación estratégica, táctica y operativa en distintos
Docencia, investigación, gestión y extensiónSamygogo
La educación ha evolucionado de un modelo tradicional de enseñanza a uno donde el docente guía el proceso educativo y acompaña al estudiante en un ambiente de valores, cultura e innovación orientado al crecimiento personal y social. Para lograr una articulación efectiva entre los programas y objetivos de las universidades, todos los involucrados deben comprometerse con estrategias que cumplan la visión de mejora continua e innovación para satisfacer las necesidades de la sociedad de manera global. Algunas estrategias que pueden desarrollar el espíritu
O documento apresenta os conceitos de processos de negócio, modelagem de processos, colaboração em processos e suporte tecnológico. Discute a importância da formalização dos processos da empresa e apresenta o modelo BPMN para modelagem. Também aborda o modelo ColabMM para avaliar a maturidade da colaboração e como sistemas de gestão de processos de negócio podem apoiar a colaboração e gestão do conhecimento.
El documento ofrece consejos sobre el liderazgo efectivo. Sugiera que un líder debe establecer un rumbo claro para el equipo siguiendo objetivos alcanzables en lugar de ideales inalcanzables, y que un líder debe enfocarse en hacer que el equipo brille en lugar de buscar su propio brillo. También enfatiza la importancia de la transparencia para el éxito del equipo.
Este documento apresenta uma aula sobre tradução de poesia de Augusto de Campos, incluindo leitura, interpretação e intertextualidade. A aula discute a teoria brasileira da tradução com foco em Augusto de Campos e apresenta poemas e vídeos sobre o poeta e a tradução. Os alunos são encarregados de pesquisar sobre o poeta E.E. Cummings para a próxima aula.
Este documento describe un programa de formación técnica en sistemas. Explica que el programa dura 6 meses de aprendizaje lectivo y 6 meses de práctica, y tiene como objetivo brindar formación en mantenimiento de equipos de cómputo e implementación de redes de datos para mejorar la competitividad del sector productivo. También describe los requisitos de ingreso, las competencias a desarrollar, los contenidos curriculares, y la estrategia metodológica centrada en el aprendizaje por proyectos.
Abengoa enfoca su crecimiento hacia la
creación de nuevas tecnologías que contribuyen al desarrollo sostenible.
La tecnología es nuestro motor de crecimiento, proporcionando soluciones
innovadoras para continuar creando valor y mantener una posición de liderazgo.
El documento describe las cualidades del sonido como la altura, intensidad, timbre y duración. Explica que la altura depende de la frecuencia de vibración y se mide en hertzios, la intensidad depende de la amplitud de vibración y se mide en desibels, el timbre depende de la combinación de armónicos y distingue los instrumentos, y la duración depende de la resistencia de las vibraciones y en música se relaciona con las figuras y el tiempo. También resume los principales géneros musicales y tip
Este documento presenta un cuestionario sobre problemas ambientales, sus causas y consecuencias en una comunidad. El cuestionario explora los problemas ambientales locales y sus orígenes, como la contaminación del agua y la disminución del caudal de los ríos. También examina el impacto del cambio climático en la agricultura y las medidas que podrían tomarse para abordar problemas como la contaminación y combatir sus efectos.
Este documento resume uma aula sobre a tradução de poesia de Augusto de Campos. Apresenta dois poemas de Campos e discute elementos formais e semânticos da tradução poética segundo a teoria brasileira da tradução de Augusto de Campos. Os alunos devem pesquisar sobre o poeta E.E. Cummings e trazer um texto sobre ele para a próxima aula.
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...Kevin Keraudren
This document presents a method for automatically localizing fetal organs in MRI scans using random forests with steerable features. The method first normalizes fetal size, then uses a classification and regression pipeline with random forests to assign voxels to organs and vote for organ centers. Features are steered based on detected landmarks like the brain to account for unknown fetal orientation. Evaluation on two datasets found the heart was localized within 10mm of ground truth in 90% of cases, suggesting it could initialize motion correction. Future work will use the detections for slice-by-slice segmentation to improve motion correction quality.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013 poster)Kevin Keraudren
This document presents a method for automatically localizing the fetal brain in MRI scans acquired as stacks of misaligned 2D slices due to fetal motion. The method first detects Maximally Stable Extremal Regions in each slice and filters by size. Histograms of SIFT features from the regions are classified using SVM to identify the brain. A 3D bounding box is fitted using RANSAC. Evaluation on 59 fetuses showed the detected box contained the entire brain in 85% of cases with a median error of 5.7mm from ground truth.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction (...Kevin Keraudren
This document proposes an automated method for segmenting the fetal brain from 2D MRI slices that have been misaligned due to fetal motion. It combines fetal brain localization in each slice using Maximally Stable Extremal Regions (MSER) and Scale-Invariant Feature Transform (SIFT) features with a patch-based propagation and Conditional Random Field (CRF) to generate a segmentation mask for each slice. It then integrates this slice-by-slice segmentation with a motion correction process to iteratively refine the segmentation as the reconstruction proceeds. The method was tested on 66 datasets ranging from 22-39 weeks gestation and produced a motion corrected volume of diagnostic quality in 85% of cases while also generating a mean 93
1) The study used fMRI to scan participants' brains while they made judgments about goal directions in a previously learned virtual environment.
2) Multivoxel pattern analysis revealed that the entorhinal/subicular region contains a neural representation of intended goal direction, as well as current facing direction. The same neural pattern was expressed for both, suggesting this region simulates future goal directions using a shared representation with current facing direction.
3) The strength of the directional representation in this region correlated with participants' performance on the task, providing evidence this region is involved in navigating to goals.
This document reviews the advances in 3D and 4D ultrasound technology for fetal cardiac scanning. Recent technologies like spatiotemporal image correlation and multiplanar reconstruction allow clinicians to generate real-time 3D/4D scans of the fetal heart. This provides significant benefits for evaluating normal and abnormal fetal heart anatomy and function. It facilitates interdisciplinary consultation, parental counseling, and professional training. While more research is still needed, 3D/4D ultrasound provides additional views of the fetal heart that can improve the accuracy of prenatal cardiac screening.
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...Nicole Freitag
This study evaluated the feasibility of using 3D MR elastography (MRE) to determine pancreatic stiffness in healthy volunteers. Twenty healthy volunteers underwent MRE exams of their pancreas at 40 Hz and 60 Hz vibration frequencies. The mean shear stiffness was calculated for different pancreatic subregions and the liver. Stiffness measurements were more consistent at 40 Hz vibration. This preliminary study suggests 3D MRE may provide reproducible pancreatic stiffness measurements and could help detect early pancreatic diseases in the future.
This thesis presents methods for automatically localizing fetal organs in MRI scans. It describes localizing the brain in 2 steps - detecting candidate brain regions using size filtering then further localizing through slice-by-slice segmentation, achieving median error of 5.7mm. For the body, it sequentially localizes organs by normalizing size by gestational age and using steerable image features informed by anatomy, detecting the heart center within 10mm in 90% of cases. This allows fully automated motion correction in over 70% of scans, presenting the first method to fully automatically localize multiple fetal organs beyond just the brain.
This study analyzed changes in the right ventricular myocardium of rats with induced pulmonary hypertension. Histological analysis found that under chronic pressure overload, myocardial fibers in the hypertensive rats reoriented longitudinally and became more aligned compared to controls. Both myofiber and collagen fiber volume significantly increased in the hypertensive rats, indicating hypertrophy and fibrosis. The relative proportion of myofibers to collagen fibers decreased slightly but not significantly. The remodeling responses aim to compensate for the increased pressure load on the right ventricle but may eventually lead to ventricular failure if pressure overload cannot be maintained.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
This study examined the relationship between white matter integrity in the cingulum bundle and preferential memory for positive images in older adults. 35 healthy older adults viewed positive, negative, and neutral images and later completed a surprise memory test. Higher fractional anisotropy (FA) values, indicating greater white matter integrity, in the total cingulum bundle and right cingulum were correlated with better memory for positive versus negative images. The results support the hypothesis that intact white matter tracts involved in executive function are associated with a positivity memory bias in older adults.
This study aimed to establish normal values for fetal cardiac axis (CAx) measurement between 11+0 and 14+6 weeks gestation. The researchers measured the CAx in 100 fetuses and found that the normal range was 34.5-56.8 degrees. CAx tended to be higher at 11+0-11+6 weeks compared to later gestations. Abnormal CAx was detected in 4 out of 6 fetuses found to have congenital heart defects. The study concludes that CAx measurement is possible in the first trimester and may help identify fetuses at risk for congenital heart defects.
Medical Image Processing Methodology for Liver Tumour Diagnosis ijsc
Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples collected from ten patients and received 90% accuracy rate.
The document evaluates the ability of T2 turbo spin echo axial and sagittal BLADE sequences to reduce or eliminate motion, pulsatile flow, and cross-talk artifacts in lumbar spine MRI examinations. Forty-four patients underwent lumbar spine MRI with both conventional and BLADE sequences. Quantitative analysis found significantly higher SNR and CNR with BLADE sequences. Qualitative analysis by radiologists also found BLADE sequences significantly superior in image quality and elimination of artifacts. The study concludes that BLADE sequences can potentially eliminate motion and other artifacts to produce high quality lumbar spine MRI images.
This document provides information about performing a fetal neurosonogram. It begins with basic embryology of the developing central nervous system. It then discusses the key features to evaluate in a neurosonogram, including biometric measurements and assessment of brain structures. Different imaging planes and techniques like 3D and tomographic ultrasound are described. Common central nervous system anomalies that can be detected on ultrasound are listed and briefly described. The document emphasizes the importance of understanding fetal brain development and utilizing multiple imaging planes and advanced techniques to thoroughly evaluate the fetal central nervous system.
Hướng dẫn Thực hành ISUOG (cập nhật 2020): Siêu âm đánh giá hệ thần kinh trun...Võ Tá Sơn
This document provides guidelines for performing ultrasound screening examinations of the fetal central nervous system. It recommends that the screening examination include evaluation of the fetal head and spine using transabdominal ultrasound to visualize the lateral ventricles, cerebellum, cisterna magna, and cavum septi pellucidi. It describes the appearance of these structures that should be assessed in the transventricular, transcerebellar, and transthalamic scanning planes between 18-37 weeks gestation. The guidelines also state that a longitudinal section of the fetal spine should be obtained when possible to screen for open and closed spinal dysraphism.
This document describes a study that evaluates different metrics for quantifying the amount of spatial information encoded by neurons. The researchers simulated neurons with various spatial firing properties and measured three information metrics: bits per spike (Ispike), bits per second (Isec), and mutual information (MI). They found Ispike and Isec did not always correlate well with decoding performance, whereas MI did. The researchers then proposed normalizing the metrics using surrogate analyses to better estimate the true spatial information. Analysis of real neuron data supported the findings from simulations.
Statistical Analysis of Skin Cell Geometry and MotionAmy Werner-Allen
1) The document summarizes research on analyzing the geometry and motion of skin cells. It examines characteristics like cell area, perimeter, number of sides, and curvature over time as cells adhere into layers. It also looks at how cells regroup after a wound and the role of proliferation.
2) To study wound healing without damaging cells, low-density cell cultures were imaged over time to analyze cluster size, cell size/shape, and density as they grow. Proliferation was examined by tracking the average number of cells per image.
3) Future work is outlined to study how proliferation and velocity depend on local density and cluster size using techniques like BrdU staining to track dividing cells. Questions about where
Strain cardíaco na avaliação da função cardíaca fetalgisa_legal
This document describes a study that used velocity vector imaging (VVI) to measure global and segmental longitudinal cardiac strain, strain rate, and velocity in 48 fetuses - 33 with normal hearts and 15 with congenital or functional heart disease. In normal fetuses, global measurements of strain, strain rate, and velocity for the left and right ventricles were similar to segmental measurements, except for velocities which differed between segments. Global measurements may provide a useful tool for quantifying fetal cardiac function. In fetuses with heart disease, global strain appeared to correlate with clinical status.
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
This study aimed to establish normative measurements of cervical spinal canal and spinal cord dimensions based on MRI scans of 140 healthy volunteers. The researchers found that dimensions varied significantly based on sex, spinal level, height, and to a lesser extent age. They defined normal ranges for sagittal diameters and cross-sectional areas of the spinal canal and cord at C1, C3, and C6 levels, accounting for sex, level, age and height. Having defined normal cervical spinal anatomy will aid radiologists in assessing potential spinal stenosis.
Similar to Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features (Miccai 2015) (20)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (Miaab 2011)Kevin Keraudren
This document summarizes a proposed method for segmenting epithelial cells in high-throughput RNAi screens using image analysis. The method uses a pipeline that includes pre-processing images using filters to reduce noise and enhance cell structures, segmenting nuclei, generating an edge map of cell-cell contacts, and performing an adaptive watershed segmentation to extract three structures: cell-cell contacts, nuclei, and cell walls. The method is shown to accurately segment these structures and provide reliable quantification of markers in different experimental conditions, distinguishing effects of depleting different actin-binding proteins on cell-cell adhesion receptors and the cytoskeleton.
This thesis presents methods for the automated localisation of organs in fetal magnetic resonance imaging (MRI) to enable automated preprocessing for motion correction. The first method localises the fetal brain independently of orientation using a Viola-Jones detector followed by classification of image regions with bundled SIFT features. This localisation of the brain is then used to steer the localisation of the heart, lungs and liver using segmentation with autocontext random forests and random forests with steerable features. Evaluation shows the brain localisation and segmentation performs as well as manual preprocessing. Preliminary results on motion correction of the fetal thorax using the heart, lung and liver localisation are also presented.
PyData London 2015 - Localising Organs of the Fetus in MRI Data Using PythonKevin Keraudren
This document summarizes an automated method for localizing fetal organs in magnetic resonance images. The method uses machine learning to sequentially localize the brain, heart, lungs and liver. It first normalizes fetal size based on gestational age. It then localizes the brain, uses this to search for the heart between two spheres. The heart location guides searching inside a third sphere for the lungs and liver. Features incorporate spatial relationships modeled by Gaussian distributions. Classification predicts organ candidates, regression refines locations, and spatial optimization selects the final detection by maximizing votes and relative organ positions. Training involves extracting random cube features around labeled pixels to classify organs.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion CorrectionKevin Keraudren
This document describes a method for automated fetal brain segmentation from 2D MRI slices in order to perform motion correction. The method uses box detection algorithms like MSER and SIFT to detect the brain region in each slice. It then trains a random forest classifier on brain and non-brain patches to perform brain extraction. Finally, it uses a conditional random field for motion correction across slices to generate a 3D volume with less artifacts from fetal movement. The results showed the proposed method produced motion-corrected volumes of diagnostic quality in 85% of test cases.
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiograph...Kevin Keraudren
Slides from Ozan Oktay at the MICCAI workshop on Sparsity Techniques in Medical Imaging (STMI2014), presenting one of the methods we used in the CETUS challenge (http://www.creatis.insa-lyon.fr/Challenge/CETUS/index.html).
Endocardial 3D Ultrasound Segmentation using Autocontext Random ForestsPresen...Kevin Keraudren
The document describes an autocontext random forest approach for endocardial 3D ultrasound segmentation. It uses successive random forest classifiers, where each gains contextual information from the previous ones. The first classifier defines the centers for the left ventricle, myocardium, and mitral valve. Subsequent classifiers perform tests on the input image, current probability maps, and geodesic distance maps. The tests compare mean intensities between offset patches from these sources. The implementation uses 4 iterations of autocontext with random forests of 20 trees and a maximal depth of 20.
Faceccrumbs: Manifold Learning on 1M Face Images, MSc group projectKevin Keraudren
MSc group project (March 2011) at Imperial College which emulated Google's People Hopper:
http://googleresearch.blogspot.co.uk/2010/03/hopping-on-face-manifold-via-people.html
Slides on Photosynth.net, from my MSc at ImperialKevin Keraudren
The document discusses 3D browsing of photo datasets using Photosynth.net. It describes the Bundler pipeline which involves extracting focal lengths from photos, finding feature points using SIFT, matching descriptors between photos, and using structure from motion to recover camera parameters and 3D point locations. It also discusses rendering the 3D scene and exploring it. Key steps include finding matches using approximate nearest neighbors and RANSAC, organizing matches into tracks, and incremental bundle adjustment to refine the model.
Slides presented at the Steiner Unit, Hammersmith Hospital, 08/06/2012Kevin Keraudren
Kevin Keraudren started a PhD in 2011 on organ localization in fetal MRI. His own research includes developing a 2D detector for fetal heads in MRI images and detecting eyes. Future plans are to validate current detectors, target other organs, and improve visualization of fetal MRI data through semi-automatic organ cataloguing.
Slides from the reading group presentation where I introduced a new Python interface for IRTK.
See http://kevin-keraudren.blogspot.co.uk/2013/12/irtk-python.html for more details and the iPython notebook demo.
Introduction to cython: example of GCoptimizationKevin Keraudren
This document discusses using Cython to interface Python with C/C++ code to improve computational performance. It provides two examples: (1) wrapping an entire C++ graph cut library in Cython, resulting in an 18 second runtime; and (2) using Cython to call a C++ graph cut function as a black box, achieving a runtime of 0.37 seconds, nearly 50 times faster. The document emphasizes that Cython can provide large speedups with relatively little code by leveraging existing optimized C/C++ implementations.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)Kevin Keraudren
Slides presented at the workshop in Microscopic Image Analysis with Applications in Biology, Heidelberg, September 2011. The associated paper can be found here: http://www.doc.ic.ac.uk/~kpk09/publications/MIAAB-2011.pdf
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
ESR spectroscopy in liquid food and beverages.pptx
Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features (Miccai 2015)
1. Automated Localization of Fetal Organs in MRI
Using Random Forests with Steerable Features
Kevin Keraudren1
, Bernhard Kainz1
, Ozan Oktay1
, Vanessa Kyriakopoulou2
,
Mary Rutherford2
, Joseph V. Hajnal2
and Daniel Rueckert1
1
Biomedical Image Analysis Group, Imperial College London
2
Department Biomedical Engineering, King’s College London
Abstract. Fetal MRI is an invaluable diagnostic tool complementary to
ultrasound thanks to its high contrast and resolution. Motion artifacts
and the arbitrary orientation of the fetus are two main challenges of fetal
MRI. In this paper, we propose a method based on Random Forests with
steerable features to automatically localize the heart, lungs and liver in
fetal MRI. During training, all MR images are mapped into a standard
coordinate system that is defined by landmarks on the fetal anatomy
and normalized for fetal age. Image features are then extracted in this
coordinate system. During testing, features are computed for different
orientations with a search space constrained by previously detected land-
marks. The method was tested on healthy fetuses as well as fetuses with
intrauterine growth restriction (IUGR) from 20 to 38 weeks of gestation.
The detection rate was above 90% for all organs of healthy fetuses in the
absence of motion artifacts. In the presence of motion, the detection rate
was 83% for the heart, 78% for the lungs and 67% for the liver. Growth
restriction did not decrease the performance of the heart detection but
had an impact on the detection of the lungs and liver. The proposed
method can be used to initialize subsequent processing steps such as
segmentation or motion correction, as well as automatically orient the
3D volume based on the fetal anatomy to facilitate clinical examination.
1 Introduction
Although the main focus of fetal Magnetic Resonance Imaging (MRI) is in imag-
ing of the brain due to its crucial role in fetal development, there is a growing
interest in using MRI to study other aspects of fetal growth, such as assessing the
severity of intrauterine growth restriction (IUGR) [4]. In this paper, we present
a method to automatically localize fetal organs in MRI (heart, lungs and liver),
which can provide automated initialization for subsequent processing steps such
as segmentation or motion correction [8].
The main challenge for the automated localization of fetal organs is the
unpredictable orientation of the fetus. Two methodological approaches can be
identified from the current literature on the localization of the fetal brain in MRI.
The first approach seeks a detector invariant to the fetal orientation [6,9,7]. The
second approach learns to detect fetuses in a known orientation and rotates
2. 2 K. Keraudren et al.
Fig. 1: Overview of the proposed pipeline for the automated localization of fetal
organs in MRI.
the test image [1]. We adopt this second approach in the present paper, but
as illustrated by steerable features [12], it is more efficient to extract features
in a rotating coordinate system than to rotate the whole 3D volume. We thus
propose to extend the Random Forests (RF) framework for organ localization
[10] with the extraction of features in a local coordinate system specific to the
anatomy of the fetus.
A RF classifier [3] is an ensemble method for machine learning which averages
the results of decision trees trained on random subsets of the training dataset,
a process called bagging. Similarly, a RF regressor is an ensemble of regression
trees. A RF classifier assigns a label to each voxel, resulting in a segmentation of
the input image while a RF regressor assigns an offset vector to each voxel, allow-
ing it to vote for the location of landmarks. Classification and regression forests
can be combined so that only voxels assigned to a certain class can vote for the
location of specific landmarks. This is of particular relevance when detecting
landmarks within the fetus whose position has little correlation with surround-
ing maternal tissues. While [5] proposed to form trees that randomly alternate
decision and regression nodes (Hough Forest), we chose to sequentially apply a
classification and a regression forest. This enables us to benefit from generic RF
implementations [11] while focusing on the key step of feature extraction.
At training time, image features are learnt in a coordinate system that is
normalized for the age of the fetus and defined by the anatomy of the fetus
(Fig. 2). At test time, assuming that the center of the brain is known, image
features are extracted in a coordinate system rotating around the brain in order
to find the heart. The lungs and liver are then localized using a coordinate
system in which one axis is parallel to the heart-brain axis. An overview of our
proposed pipeline is presented in Fig. 1. In the remainder of this paper, we will
present our proposed method in Section 2 and evaluate it in Section 3 on two
datasets of MRI scans. The first dataset, used for training and leave-one-out
cross validation, consists of scans of 30 healthy and 25 IUGR subjects without
significant motion artifacts. The second dataset (64 subjects) is used to evaluate
the performance of the proposed method in the presence of motion artifacts.
2 Method
3. Automated Localization of Fetal Organs in MRI 3
u0u0
v0v0
w0w0
liverliver
Coronal plane
u0u0
w0w0
v0v0
brainbrain
heartheart
Sagittal plane
v0v0
w0w0
u0u0
heartheart
rightright
lunglung
Transverse plane
Fig. 2: Average image of 30 healthy fetuses after resizing (Section 2) and align-
ment defined by anatomical landmarks (Section 2). It highlights the character-
istic intensity patterns in fetal MRI for the brain, heart, lungs and liver.
Preprocessing: There is a large variability in the MR scans of fetuses due
to fetal development. In particular, the length of the fetus increases by 50%
between 23 weeks and term (40 weeks) [2]. We thus propose to normalize the
size of all fetuses by resampling the images to an isotropic voxel size sga that is a
function of the gestational age (GA), so that a fetus of 30 weeks is resampled to
a voxel size s30: sga = CRLga/CRL30 ×s30, where CRL denotes the crown-rump
length [2]. This differs from previously proposed methods which either ignored
the GA [1,6,7] or only used it to define size constraints [9]. Moreover, fetal MRI
is typically acquired as stacks of 2D images that freeze in-plane motion but
form a 3D volume corrupted by fetal and maternal motion. The images are thus
preprocessed using median filtering to attenuate motion artifacts.
Detecting candidate locations for the heart: In theory, a detector could be
trained on aligned images while at test time, it would be applied to all possible
rotated versions of the 3D volume. However, in practice it would be too time
consuming to explore an exhaustive set of rotations. In particular, the integral
image that is used to extract intensity features would need to be computed
for all tested rotations. Instead of rotating the image itself, we thus propose to
rotate the sampling grid of the image features. This is done by defining a local
orientation of the 3D volume for every voxel.
When training the classifier, all voxels are oriented using the anatomy of the
fetus (Fig. 2 and Fig. 3.a): u0 points from the heart to the brain, v0 from the left
lung to the right lung and w0 from back to front (w0 = u0 ×v0). When applying
the detector to a new image, in theory, all possible orientations (u, v, w) should be
explored. However, assuming that the location of the brain is already known [9],
u can be defined as pointing from the current voxel x to the brain (Fig. 3.b). In
this case, only the orientation of the plane Px,u passing through x and orthogonal
to u remains undefined. (v, w) are then randomly chosen so that they form an
orthonormal basis of Px,u. The rationale for this definition of u is that when x
is positioned at the center of the heart, Px,u is the transverse plane of the fetal
anatomy (Fig. 2). To address the sensitivity to the brain localization, a random
offset is added to the estimated brain center with a 10mm range comparable to
the prediction errors reported in [9]. Additionally, knowing the location of the
4. 4 K. Keraudren et al.
u0u0v0v0
u0u0v0v0
u0u0v0v0
uu
vv
uu
vv
uu
vv
(a) (b)
Fig. 3: (a) When training the classifier, features (light blue squares) are extracted
in the anatomical orientation of the fetus: u0 points from the heart (green) to the
brain (red), and v0 points from the left lung (blue) to the right lung (yellow). The
pink contour corresponds to the liver. (b) At testing time, features are extracted
in a coordinate system rotating around the brain.
brain, the search for the heart only needs to explore the image region contained
between two spheres of radii R1 and R2 (Fig. 4) that are independent of the GA
thanks to the size normalization.
The localization of the heart is performed in two stages: a classification forest
is first applied to obtain for each voxel x a probability p(x) of being inside the
heart (Fig 5.a). All voxels with p(x) > 0.5 are then passed on to a regression
forest, which predicts for each of them the offset to the center of the heart.
Using the predicted offsets, the probabilities are then accumulated in a voting
process, and the voxels receiving the highest sum of probabilities are considered
as candidate centers for the heart (Fig 5.b). During classification, in order to
increase the number of true positives, an initial random orientation of the plane
Px,u is chosen and the detector is successively applied by rotating (v, w) with
an angular step θ. The value of (v, w) maximizing the probability of the current
voxel to be inside the heart is selected, and only this orientation of Px,u is passed
on to the regression step.
Four types of features are used in the RFs: tests on image intensity, gradient
magnitude, gradient orientation and distance from the current voxel to the brain.
In order to be invariant to intensity shifts [10], image intensity or gradient mag-
nitude features select two axis-aligned cube patches of random sizes at random
offsets from the current voxel and compare their mean intensity. All features are
extracted in the local orientation (u, v, w). Similarly, during the voting process,
the predicted offsets are computed with the same local orientation.
Detecting candidate locations for the lungs and liver: For each candidate
location of the heart, candidate centers for the lungs and liver are detected.
Similarly to the heart detection, a classification RF first classifies each voxel
as either heart, left lung, right lung, liver or background. For each organ, a
5. Automated Localization of Fetal Organs in MRI 5
R1R1
R2R2
R
3
R
3
Fig. 4: Knowing the location of the brain, the search for the heart can be re-
stricted to a narrow band within two spheres of radius R1 and R2 (red). Sim-
ilarly, knowing the location of the heart, the search for the lungs and the liver
can be restricted to a sphere of radius R3 (green).
regression RF is then used to vote for the organ center. During training, the
images are oriented according to the fetal anatomy (Fig. 2). During testing, both
the location of the brain and heart are known and only the rotation around the
heart-brain axis needs to be explored. For each voxel x, we can hence set u = u0,
and only the orientation of the plane Px,u0
remains unknown. (v, w) are thus
randomly chosen to form an orthonormal basis of Px,u0
.
In the set of features for the RFs, the distance from the current voxel to
the brain, which was used in the detection of the heart, is now replaced by the
distance to the heart. Additionally, the projection of the current voxel on the
heart-brain axis is used in order to characterize whether the current voxel is be-
low or above the heart relative to the brain. Similarly to the heart detection, the
detector is run with an angular step θ. For each voxel, the value of θ maximizing
the probability of being inside one of the organs of the fetus is selected.
Spatial optimization of candidate organ centers: The voting results from
the regression RF are first smoothed with a Gaussian kernel. Local maxima
are then extracted to form a set of candidates xl for the center of each organ,
l ∈ L = { heart, left lung, right lung, liver }, along with their individual scores
p(xl). Each combination of candidate centers is assigned a score (Eq. 1) based
on the individual scores p(xl) and the spatial configuration of all organs:
l∈L
λp(xl) + (1 − λ)e− 1
2 (xl−¯xl) Σ−1
l (xl−¯xl)
(1)
where ¯xl is the mean position and Σl the covariance matrix for the organ l in
the training dataset, computed in the anatomical coordinate system of the fetus.
λ is a weight between individual scores and the spatial configuration of organs.
Once the organ centers have been localized, the voting process can be reversed
in order to obtain a rough segmentation (Fig. 5.c).
6. 6 K. Keraudren et al.
(a) (b) (c)
Fig. 5: (a) Probability of a voxel to be inside the heart. (b) Accumulated regres-
sion votes for the center of the heart. (c) After all organs have been located,
the voting process can be reversed, resulting in a rough segmentation for each
organ: heart (blue), left lung (red), right lung (green), liver (yellow).
3 Experiments
Datasets: Two datasets of 1.5T T2-weighted ssTSE images were used in this
paper. The first dataset was used to train and validate the model in a leave-
one-out cross validation, in the absence of motion artifacts. It consists of 55
scans covering the whole uterus for 30 healthy subjects and 25 IUGR subjects
(scanning parameters: TR 1000ms, TE 98ms, 4mm slice thickness, 0.4mm slice
gap). The second dataset is used to test the model in the presence of motion
artifacts. There are 224 scans, from 64 healthy subjects, which do not all fully
cover the fetal body (scanning parameters: TR 15000ms, TE 160ms, 2.5mm slice
thickness, 1.5mm slice overlap). A ground truth segmentation for all fetal organs
was provided by an expert for the first dataset. Due to the large number of images
and the presence of motion artifacts, only the center of each organ has been
manually annotated in the second dataset. The location of the brain is passed
to the detector for the first dataset in order to evaluate the proposed method on
its own while the method of [9] is used to localize the brain automatically in the
second dataset, thus evaluating a fully automated localization of fetal organs.
Both datasets cover the range of gestational ages from 20 to 38 weeks.
Results: The results are reported as detection rates in Table 1, where a de-
tection is considered successful for the organ l if the predicted center is within
the ellipsoid defined by (xl − yl) Σ−1
l (xl − yl) < 1 where yl is the ground truth
center. For the first dataset, Σl is the covariance matrix of the ground truth
segmentation while for the second dataset, it is defined from training data as
in Section 2. The distance error between the predicted centers and their ground
truth are reported in Fig. 6. Although healthy and IUGR subjects were mixed
in the training data, results are reported separately to assess the performance of
the detector when fetuses are small for their gestational age. Training two detec-
tors on each subset of the data did not improve the results. The higher detection
7. Automated Localization of Fetal Organs in MRI 7
Table 1: Detection rates for each organs.
Heart Left lung Right lung Liver
1st
dataset: healthy 90% 97% 97% 90%
1st
dataset: IUGR 92% 60% 80% 76%
2nd
dataset 83% 78% 83% 67%
Left
lung
Right
lung
Heart Liver
0
5
10
15
20
25
30
35
40
Distanceerror(mm)
1st
dataset: healthy
Left
lung
Right
lung
Heart Liver
0
5
10
15
20
25
30
35
40
1st
dataset: IUGR
Left
lung
Right
lung
Heart Liver Brain
0
5
10
15
20
25
30
35
40
2nd
dataset
Fig. 6: Distance error between the predicted organ centers and their ground truth
for the first dataset (30 healthy fetuses and 25 IUGR fetuses) and the second
dataset (64 healthy fetuses).
rate for the lungs than for the heart for the healthy subjects of the first dataset
(Table 1) is due to the detector overestimating the size of the heart (Fig 5.c),
which can be linked to the signal loss when imaging the fast beating heart. A
decrease in performance can be observed for the detection of the lungs in IUGR
fetuses, which can be associated with a reduction in lung volume reported in [4].
The asymmetry in the performance of the detector between the left and right
lungs can be explained by the presence of the liver below the right lung, leading
to a stronger geometric constraint in Eq. 1.
For the second dataset, the largest error for the detection of the brain detec-
tion using the method of [9] is 21mm, which is inside the brain and is sufficient
for the proposed method to succeed. In 90% of cases, the detected heart center
is within 10mm of the ground truth, which suggests that the proposed method
could be used to provide an automated initialization for the motion correction of
the chest [8]. Beside motion artifacts, the main difference between the first and
second dataset is that the second dataset does not fully cover the fetal abdomen
in all scans, which could explain its lower performance when detecting the liver.
Training the detector takes 2h and testing takes 15min on a machine with 24
cores and 128 GB RAM. The parameters in the experiments were chosen as
follows: s30 = 1mm, maximal patch size 30 pixels, maximal offset size 20 pixels,
λ = 0.5, θ = 90◦
.
8. 8 K. Keraudren et al.
4 Conclusion
We presented a pipeline which, in combination with automated brain detection,
enables the automated localization of the lungs, heart and liver in fetal MRI.
The localization results can be used to initialize a segmentation or motion cor-
rection, as well as to orient the 3D volume with respect to the fetal anatomy to
facilitate clinical diagnosis. The key component to our method is to sample the
image features in a local coordinate system in order to cope with the unknown
orientation of the fetus. During training, this coordinate system is defined by
the fetal anatomy while at test time, it is randomly oriented, within constraints
set by already detected organs. In future work, the possibility to merge the
classification and regression steps into a Hough Forest [5] will be investigated.
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