The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisMD Abdullah Al Nasim
Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
At the end of this lesson, you should be able to;
define segmentation.
Describe edge based in segmentation.
describe thresholding and its properties.
apply edge detection and thresholding as segmentation techniques.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
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
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular...Wookjin Choi
To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisMD Abdullah Al Nasim
Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
At the end of this lesson, you should be able to;
define segmentation.
Describe edge based in segmentation.
describe thresholding and its properties.
apply edge detection and thresholding as segmentation techniques.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
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
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular...Wookjin Choi
To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy.
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...Wookjin Choi
Abstract
Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they should be robust (relatively invariant or unbiased) to tumor size variations and not correlated (non-redundant) with the normal lung volume of interest, i.e., volume of the peri-tumoral region.
Methods: CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated with spheres (diameters 10 to 60 mm) and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the peri-tumoral region (uniform 30 mm expansion around the GTV in the lung). The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%), which was chosen at the nRoA of the volume of the peri-tumoral region with modification based on the cumulative graph of features vs. nRoA. A feature was regarded as not correlated with the volume of the peri-tumoral region when their correlation was lower than 0.70.
Results: 16 of the 27 texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except cluster shade, cluster prominence, and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were unrobost. None of the robust features was correlated with the volume of the peri-tumoral region. No feature showed statistically significant differences (P<0.05) on GTV location (upper vs. lower lobe).
Conclusion: We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD. Particularly, GLRM high gray-level emphasis features were robust and characterized the radiologic manifestations of pulmonary abnormalities.
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...Wookjin Choi
Abstract
Purpose/Objective(s)
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
Materials/Methods
The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres (diameters 10 to 60 mm) placed in the lung contralateral to the tumor. Twenty-seven texture features (9 from intensity histogram, 8 from the gray-level co-occurrence matrix [GLCM], and 10 from the gray-level run-length matrix [GLRM]) were extracted from [lung – GTV]. The Bland-Altman method was applied to measure the normalized range of agreement (nRoA) of each texture feature when GTV size varied. A feature was considered as robust when its nRoA was less than that of [lung – GTV] volume (8.8%) and regarded as not correlated when their absolute correlation coefficient was lower than 0.70.
Results
Eighteen texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except energy and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were nonrobust. Particularly, all three low gray-level emphasis features had extremely large nRoAs (∼30%), indicating huge variations when GTV size changed. None of the robust features was correlated with the normal lung [lung – GTV] volume, suggesting that they can provide additional information. Three nonrobust features (sum and two nonuniformity features) were highly correlated with the normal lung volume. None feature showed statistically significant differences (P < 0.05) with respect to GTV location (upper vs. lower lobe).
Conclusion
We identified 18 robust lung CT texture features which were invariant to varying tumor volumes. Particularly the three GLRM high gray-level emphasis features can characterize the radiologic manifestations of pulmonary abnormalities. Hence these features can be further examined for the prediction of the RILD.
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...Wookjin Choi
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Identification of Robust Normal Lung CT Texture FeaturesWookjin Choi
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4955803
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...Wookjin Choi
To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA).
http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4958261
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...Wookjin Choi
Purpose/Objectives: To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic adenocarcinoma (PDA).
Materials/Methods: Ten PDA patients were enrolled and underwent three CT scans: a 4D-CT immediately following a CE 3D-CT, and an individually optimized CE 4D-CT using a test injection to estimate the peak contrast enhancement time and to optimize the delay time. Three physicians contoured the tumor and pancreatic tissues. We compared image quality scores, tumor volume, motion, image noise, tumor-to-pancreas contrast, and contrast-to- noise ratio (CNR) in the three CTs. We also evaluated inter-observer variations in contouring the tumor using simultaneous truth and performance level estimation (STAPLE).
Results: The average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, p=0.47), and both were significantly better than that for 4D-CT (2.6, p<0.001). The tumor-to- pancreas contrast in CE 3D-CT and CE 4D-CT were comparable (15.5 and 16.7 HU, p=0.71), and the later was significantly higher than that in 4D-CT (9.2 HU, p=0.03). Image noise in CE 3D-CT (12.5 HU) was significantly lower than that in CE 4D-CT (22.1 HU, p<0.001) and 4D-CT (19.4 HU, p=0.005). The CNR in CE 3D-CT and CE 4D-CT were comparable (1.4 and 0.8, p=0.23), and the former was significantly better than that in 4D-CT (0.6, p=0.04). The average tumor volume was smaller in CE 3D-CT (29.8 cm 3 ) and CE 4D-CT (22.8 cm 3 ) than in 4D-CT (42.0 cm 3 ), though the differences were not statistically significant. The tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, p=0.23). The inter-observer variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%), and the former was significantly smaller than that of 4D-CT (55.6%, p=0.047).
Conclusions: The CE 4D-CT demonstrated largely comparable characteristics to the CE 3D-CT. It has high potential for simultaneously delineating the tumor and quantifying the tumor motion with a single scan.
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT RadiomicsWookjin Choi
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Prediction of Lung Cancer Using Image Processing Techniques: A Reviewaciijournal
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the
cells are overlapped each other. The image processing techniques are mostly used for prediction of lung
cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer
various features are extracted from the images therefore, pattern recognition based approaches are useful
to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous
researcher using image processing techniques is presented. The summary for the prediction of lung cancer
by previous researcher using image processing techniques is also presented.
Prediction of Lung Cancer Using Image Processing Techniques: A Reviewaciijournal
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the
cells are overlapped each other. The image processing techniques are mostly used for prediction of lung
cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer
various features are extracted from the images therefore, pattern recognition based approaches are useful
to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous
researcher using image processing techniques is presented. The summary for the prediction of lung cancer
by previous researcher using image processing techniques is also presented.
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Interpretable Spiculation Quantification for Lung Cancer ScreeningWookjin Choi
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Classification techniques using gray level co-occurrence matrix features for ...IJECEIAES
Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
USING DISTANCE MEASURE BASED CLASSIFICATION IN AUTOMATIC EXTRACTION OF LUNGS ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...semualkaira
Computer-assisted detection (CAD) systems
based on artificial intelligence (AI) utilizing convolutional neural
networks (CNN) have demonstrated successful outcomes in diagnosing lung lesions in several studies. This study aims to report
the clinical implication in a real clinical practice, demonstrating
efficacy in commonly encountered cases and categorizing the lung
nodules that they can effectively detect.
Similar to Image processing in lung cancer screening and treatment (20)
Deep Learning-based Histological SegmentationDifferentiates Cavitation Patte...Wookjin Choi
Unsupervised segmentation (unlabeled regions of interest, ROIs) and autoencoder (AE)-based classification were used to classify differences in cavitation patterns in knees and digits using the stained images (n=20-30 images/group).
Each image was divided into 256 x 256 pixel patches, and a convolutional neural network (CNN)-based unsupervised segmentation was used to identify ROIs. These patches were subsequently fed into a CNN-based AE whose latent space layer was connected to a classifier for input patch classification.
The AE was trained using the ROIs identified by the unsupervised segmentation, and the image classes were used to train the classifier. Whole image classifications were determined by maximum voting of the patch results and evaluated by accuracy.
Artificial Intelligence To Reduce Radiation-induced Cardiotoxicity In Lung Ca...Wookjin Choi
Traditionally, radiation-induced cardiotoxicity has been studied using cardiac radiation doses rather than functional imaging. We developed artificial intelligence (AI) models based on novel cardiac delta radiomics using pre- and post-treatment FDG-PET/CT scans to predict overall survival in lung cancer patients undergoing radiotherapy. We identified four clinically relevant delta radiomics features with the AI prediction models. The best model achieved an AUC of 0.91 on the training set and 0.87 on the test set. We are a pioneering group in AI for functional cardiac imaging. If validated, this approach will enable to use standard PET/CT scans as functional cardiac imaging with good predictive AUC for OS, as well as provide automated methods to provide functional cardiac information for clinical outcome prediction AI in lung cancer patients.
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...Wookjin Choi
Purpose/Objective(s)
Traditional methods of evaluating cardiotoxicity focus solely on radiation doses to the heart and do not incorporate functional imaging information. Functional imaging has great potential to improve the ability to provide early prediction for cardiotoxicity for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained as part of standard staging work up for lung cancer patients. Although FDG PET/CT scans are typically used to evaluate the tumor, imaging guidelines note that FDG PET/CT scans are an FDA-approved method to image for cardiac inflammation, and studies have noted that the PET cardiac signal can be predictive of clinical outcomes. The purpose of this work was to develop a radiomics model to predict clinical cardiac assessment of standard of care FDG PET/CT scans.
Materials/Methods
The study included 100 consecutive lung cancer patients treated with radiotherapy who underwent standard pre-treatment FDG-PET/CT staging scans. A clinician reviewed the PET/CT scans per clinical cardiac assessment guidelines and classified the cardiac uptake as: 0 = uniform diffuse, 1 = absent, 2 = heterogeneous, with event rates of 20%, 44%, and 35%, respectively. The heart was delineated and 200 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. We divided the data into an 80% training set and a 20% test set to train and evaluate the classification models. Feature reduction was carried out using the Wilcoxon test (with Bonferroni adjusted p<0.05), hierarchical clustering, and Recursive Feature Elimination. Two automatic machine learning (AutoML) frameworks were used to determine classification models: a Random Forest Classifier (Tree-based Pipeline Optimization Tool, TPOT) and Linear Discriminant Analysis (AutoSklearn). 10-fold cross validation was carried out for training and the accuracy of the ability of the models to predict for clinical cardiac assessment is reported.
Results
Fifty-one independent radiomics features were reduced to 3 clinically pertinent features (PET 2D Skewness, PET Grey Level Co-occurrence Matrix Correlation, and PET Median) using feature reduction techniques. The model selected by TPOT showed 89.8% predictive accuracy in the cross validation of the training set and 85% predictive accuracy on the test set. The model selected by AutoSklearn showed 89.7% predictive accuracy in the cross validation of the training set and 80% predictive accuracy on the test set.
Conclusion
The novelty of this work is that it is the first study to develop and evaluate functional cardiac radiomic features from standard of care FDG PET/CT scans with the data showing good predictive accuracy with clinical imaging evaluation. If validated, the current work provides automated methods to provide functional cardiac information using standard of care imaging that can be used as an imaging biomarker for early clinical toxicity prediction for lung cancer patients.
Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation...Wookjin Choi
Purpose/Objective(s)
MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans.
Materials/Methods
The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected.
Results
Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9
Conclusion
We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...Wookjin Choi
AAPM2023_SU-300-IePD-F6-4
Purpose: Traditional methods of evaluating cardiotoxicity rely on cardiac radiation doses and do not incorporate functional imaging. Cardiac functional imaging can improve the ability to provide early prediction for clinical outcomes for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained for staging and disease assessment after treatment. Although FDG PET/CT scans are typically used to evaluate the tumor, studies have shown that the PET cardiac signal is predictive of clinical outcomes. Our study aimed to develop novel functional cardiac delta radiomics using pre and post-treatment FDG PET/CT scans to predict for overall survival (OS).
Methods: We conducted a study of 109 lung cancer patients who underwent standard FDG-PET/CT scans pre- and post-radiotherapy. Data from ACRIN 6668 (N=70) and an investigator-initiated lung cancer trial (N=39) for functional avoidance radiotherapy were used. The heart was delineated, and 200 cardiac CT and PET functional radiomics features were selected. Delta radiomics was calculated as the change between pre- and post-PET/CT. The data were divided into 80%/20% training/test set, and feature reduction was performed using Wilcoxon test, hierarchical clustering, and recursive feature elimination. A Gradient Boosting Classifier machine learning model evaluated the ability of the delta PET/CT cardiac radiomics to predict for OS using 10-fold cross-validation for training and area-under-the-curve (AUC) for model assessment.
Results: Median survival was 431 days (range 144 to 1640 days). 4 clinically relevant delta features were identified: pre-CT_Maximum, post-CT_Minimum, delta-CT_GLRM_Run_Variance, delta-PET_GLRM_Run_Entropy. The model showed an AUC of 0.91 on the training set and an AUC of 0.87 on the test set.
Conclusion: This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans with data showing good predictive AUC for OS. If validated, this work provides automated methods to provide functional cardiac information for clinical outcome prediction in lung cancer patients.
Automatic motion tracking system for analysis of insect behaviorWookjin Choi
Undergraduate research.
We present a multi-object tracking system to track small insects such as ants and bees. Motion-based object tracking recognizes the movements of objects in videos using information extracted from the given video frames. We applied several computer vision techniques, such as blob detection and appearance matching, to track ants. Moreover, we discussed different object detection methodologies and investigated the various challenges of object detection, such as illumination variations and blob merge/split. The proposed system effectively tracked multiple objects in various environments.
Assessing the Dosimetric Links between Organ-At-Risk Delineation Variability ...Wookjin Choi
Purpose: To determine the relative dosimetric impact of delineation variability (DV) when inter-observer and inter-technique planning variability (PV), and setup variability (SV) with are considered.
Methods: 409 plans for a single head-and-neck patient from the 2017 Radiation Knowledge plan competition were used. Plans were created with Eclipse (N=227), Pinnacle (N=49), RayStation (N=25), Monaco (N=75), and TomoTherapy (N=33) with delivery techniques conventional linac IMRT (N=142), volumetric modulated arc therapy (VMAT, N=234), and helical TomoTherapy (N=33). All plans were optimized using a consistent set of target volumes and a single OAR structure set. Four additional OAR structure sets were contoured by radiation oncologists (N=2) and medical physics residents (N=2) who had completed head-and-neck contouring training. Probabilistic DVHs, dose-volume coverage maps (DVCM), which shows the probability of achieving a dose metric, were computed for each OAR on the following scenarios: SV alone (N=1000), SV+PV (N=1000*409), SV+DV (N=1000*5), SV+PV+DV (total variability [TV], N=1000*409*5). Analysis focused on the probability of exceeding the maximum dose constraint exceeded 5% for each OAR.
Results: The primary source of variability was PV, which was expected due to inter-observer planning abilities and preferences during the optimization planning process, even when all participants utilized the same constraints. The parotid had the most significant interquartile range (IQR) on the PV scenario. Conversely, adding SV, DV, and TV each reduced the IQR, showing a washing out effect on the DVCM.
Conclusion: Assessment of OAR sensitivity to DV will be highly sensitive to the specific planning technique and planner, likely requiring plan-specific assessment of in-tolerance delineation variations. Incorporation SV and DV variabilities in plan assessments washes out their relative impacts on maximum dose.
Simulation of Realistic Organ-At-Risk Delineation Variability in Head and Nec...Wookjin Choi
(Sunday, 7/14/2019) 4:00 PM - 5:00 PM
Room: 225BCD
Purpose: To simulate realistic manual delineation (MD) organ-at-risk (OAR) delineation variability (DV) the purpose of quantifying DV’s dosimetric impact.
Methods: Fourteen independent MD head-and-neck OAR structure sets (SS) were obtained from the ESTRO Falcon group. Seven OARs were available (BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid). A consensus MD SS was generated by the simultaneous truth and performance level estimation (STAPLE) method. MD DV was evaluated with respect to the STAPLE SS using the Dice coefficient and Hausdorff distance (HD) geometric similarity metrics. DVs were simulated using auto-delineation (AD)
methods: an average surface of standard deviation (ASSD) method, GrowCut segmentation, and a random walker (RW) segmentation. Each OAR AD was repeated five times with a different seed or variability level. Dice and HD were computed for each OAR AD with respect to the STAPLE SS. Dosimetric analysis was achieved by intercomparing dose-volume histograms (DVH) from a plan developed with a reference MD SS with DVHs for each MD and AD. DVH confidence bands are reported for MD and each AD method.
Results: The MD Dice was 0.7±0.2 (μ±σ). AD Dice values (ASSD, GrowCut, and RW) were 0.5±0.2, 0.7±0.2, and 0.8±0.1, respectively. HDs were 35.4±45.2, 27.3±19.1, 29.3±19.9, and 14.6±10.3. The simulated DV increased with increasing the seed standard deviations or variability level. The dosimetric effect was largest for MD DVs (larger OAR DVH confidence intervals and larger HD), even though the MD Dice was greater than the ASSD and GrowCut Dice values. GrowCut DV resulted in less dosimetric variation than RW, unlike the geometric indices.
Conclusion: We developed a framework to simulate DVs and demonstrated its feasibility. ADs were able to simulate different magnitudes of DVs, but did not replicate the dosimetric consequences of human delineation variability. The correlation between geometric similarity metrics and dosimetric consequences of DV is poor.
Quantitative image analysis for cancer diagnosis and radiation therapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Interpretable Spiculation Quantification for Lung Cancer ScreeningWookjin Choi
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Quantitative Image Analysis for Cancer Diagnosis and Radiation TherapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Robust breathing signal extraction from cone beam CT projections based on ada...Wookjin Choi
Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques
Ming Chao, Jie Wei, Tianfang Li, Yading Yuan, Kenneth E Rosenzweig and Yeh-Chi Lo
Department of Radiation Oncology, Mount Sinai Medical Center, New York, NY 0029, USA
Department of Computer Science, City College of New York, New York, NY 10031, USA
Department of Radiation Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
Dual energy CT in radiotherapy: Current applications and future outlookWookjin Choi
Dual energy CT in radiotherapy: Current applications and future outlook
Wouter van Elmpt, Guillaume Landry, Marco Das, Frank Verhaegen
Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands; Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching b. München, Germany; Department of Radiology, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands; and Medical Physics Unit, Department of Oncology, McGill University, Montréal, Canada
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
2. Introduction
Lung cancer is the leading cause of cancer deaths.
Most patients diagnosed with lung cancer already have advanced
disease
40% are stage IV and 30% are III
The current five-year survival rate is only 16%
(a) male (b) female
Trends in death rates for selected cancers, United States, 1930-2008 2
3. Lung cancer screening and treatment
Lung Cancer
Screening
• Nodule detection
and diagnosis
• Biopsy
• …
Lung Cancer
Treatment
• Surgery
• Chemotherapy
• Radiotherapy
• Tumor Response
• …
Computer-Aided De
tection (CADe) and
Diagnosis (CADx)
Image-guided Radiot
herapy and Quantitat
ive Assessment
3
4. Image processing in Lung cancer screening and
treatment
Feature Extraction and Analysis
Nodule (tumor) segmentation
Lung segmentation
Image Registration
4
13. Pulmonary Nodule Detection CAD system
CAD systems Lung segmentation Nodule Candidate Detection False Positive Reduction
Suzuki et al.(2003)[3] Thresholding Multiple thresholding MTANN
Rubin et al.(2005)[4] Thresholding Surface normal overlap
Lantern transform and rule-
based classifier
Dehmeshki et al.(2007)[5] Adaptive thresholding Shape-based GATM Rule-based filtering
Suarez-Cuenca et al.(2009)[6]
Thresholding and 3-D
connected component
labeling
3-D iris filtering
Multiple rule-based LDA
classifier
Golosio et al.(2009)[7] Isosurface-triangulation Multiple thresholding Neural network
Ye et al.(2009)[8]
3-D adaptive fuzzy
segmentation
Shape based detection
Rule-based filtering and
weighted SVM classifier
Sousa et al.(2010)[9] Region growing Structure extraction SVM classifier
Messay et al.(2010)[10]
Thresholding and 3-D
connected component
labeling
Multiple thresholding and
morphological opening
Fisher linear discriminant and
quadratic classifier
Riccardi et al.(2011)[11] Iterative thresholding
3-D fast radial filtering and
scale space analysis
Zernike MIP classification
based on SVM
Cascio et al.(2012)[12] Region growing Mass-spring model
Double-threshold cut and
neural network 13
14. Genetic Programming based Classifier
Wook-Jin Choi, Tae-Sun Choi, “Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on
computed tomography images”, Information Sciences, Vol. 212, pp. 57-78, December 2012, doi: http://dx.doi.org/10.1016/j.ins.2012.05.008
Feature spaces for four types of features
2-D geometric feature 3-D geometric feature
2-D intensity-based statistical feature 3-D intensity-based statistical feature
Genetic programming classifier learning
Classification space
GP based classification expression in tree shape
Optimal multi-thresholding based Nodule candidates de
tection.
14
15. Hierarchical Block-image Analysis
Wook-Jin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block
Classification Approach”, Entropy, Vol. 15, No. 2, pp. 507-523, February 2013, doi:http://dx.doi.org/10.3390/e15020507
ROC curves of the SVM classifiers with respect to three different kernel functions,
SVM-r: radial basis function, SVM-p: polynomial function, and SVM-m:
Minkowski distance function; (a) p = 0:25 and (b) p = 1.
Result images after block splitting with respect to various block sizes
The entropy histograms of block-images for five different block sizes
(x-axis : entropy value, y-axis : number of blocks, (a) 32, (b) 24, (c) 16, (d) 12, and (e) 8)
15
16. θ φ
θ φ
Three-dimensional Shape-based Feature Descriptor
Wook-Jin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based Feature Descriptor”, Computer
Methods and Programs in Biomedicine, Vol. 113, No. 1, January 2014, pp. 37–54, doi: http://dx.doi.org/10.1016/j.cmpb.2013.08.015
Surface saliency weighted surface
normal vectors
Two angular histograms of the
surface normal vectors
θ φ
ROC curves of the SVM classifiers with respect to three different kernel
functions, SVM-r: radial basis function, SVM-p: polynomial function,
and SVM-m: Minkowski distance function; (a) p = 0:25 and (b) p = 1.
FROC curves of the proposed CAD system with
respect to three different dimensions of AHSN
features
θ φ
θ φ
Feature optimization with wall detection a
nd elimination algorithm
3D shape-based feature descriptor
High surface saliency
value
Low surface saliency
value
16
17. Comparative Analysis
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14 16
Overallsensitivity
False positives per scan
Suzuki et al. (2003)
Dehmeshki et al. (2007)
Suarez-Cuenca et al.
(2009)
Golosio et al. (2009)
Ye et al. (2009)
Messay et al. (2010)
Riccardi et al. (2011)
Cascio et al. (2012)
Choi et al. (2012)
Choi et al. (2013)
Choi et al. (2014)
17
18. Computer Aided Diagnosis
Once the lung nodules are detected and segmented from the
corresponding chest images
The next task is to determine whether the detected nodules
are malignant or benign
The malignancy of lung nodules correlates highly with
Geometrical size
Shape
Appearance descriptors
Ayman El-Baz, Garth M. Beache, Georgy Gimel'farb, et al., “Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Metho
dologies,” International Journal of Biomedical Imaging, vol. 2013, Article ID 942353, 46 pages, 2013. doi:10.1155/2013/942353 18
19. Computer Aided Diagnosis
Typical computer-aided diagnosis (CAD) system for lung cancer. The input of a CAD system i
s the medical images obtained using an appropriate modality. A lung segmentation step is used
to reduce the search space for lung nodules. Nodule detection is used to identify the locations
of lung nodules. The detected nodules are segmented. Then, a candidate set of features, such a
s volume, shape, and/or appearance features, are extracted and used for diagnosis.
Ayman El-Baz, Garth M. Beache, Georgy Gimel'farb, et al., “Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Meth
odologies,” International Journal of Biomedical Imaging, vol. 2013, Article ID 942353, 46 pages, 2013. doi:10.1155/2013/942353 19
20. Computer Aided Diagnosis
Ayman El-Baz, Garth M. Beache, Georgy Gimel'farb, et al., “Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Meth
odologies,” International Journal of Biomedical Imaging, vol. 2013, Article ID 942353, 46 pages, 2013. doi:10.1155/2013/942353
Study Purpose Method Database Observations
Jennings
et al.
To retrospectively
determine the distribution
of stage I lung cancer
growth rates with serial
volumetric CT
Volumetric
measurement
149
patients
At serial volumetric CT measurements, there was wide
variability in growth rates. Some biopsy-proved cancers
decreased in volume between examinations
Zheng et
al.
To simultaneously segment
and register lung and
tumor in serial CT data
Nonrigid
transformation
on lung
deformation and
rigid structure
on the tumor
6 volumes
of 3
patients,
83 nodules
The mean error of boundary distances between automatic
segmented boundaries of lung tumors and manual
segmentation is 3.50 pixels. The mean and variance of
percentages of the nodule volume variations caused by
errors in segmentation are 0.8 and 0.6
Marchian
ò et al.
To assess in vivo
volumetric repeatability of
an automated algorithm for
volume estimation.
Semiautomatic
volumetric
measurement
101
subjects,
233
nodules
The 95% confidence interval for difference in measured
volumes was in the range of ±27%. About 70% of
measurements had a relative difference in nodule volume
of less than 10%
El-Baz et
al.
To monitor the
development of lung
nodules
Global and local
registration, GR
volumetric
measurement
135 LDCT
from 27
subjects,
27 nodules
All the 27 nodules are correctly classified based on GR
measurements over 12 months
Growth-rate-based methodologies for following up pulmonary nodules.
20
21. Computer Aided Diagnosis
Ayman El-Baz, Garth M. Beache, Georgy Gimel'farb, et al., “Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Meth
odologies,” International Journal of Biomedical Imaging, vol. 2013, Article ID 942353, 46 pages, 2013. doi:10.1155/2013/942353
Study Purpose Method Database Observations
Suzuki et al.
To classify nodules into
Benign or Malignant
Multiple MTANNs using
pixel values in
a subregion
Thick-slice (10 mm) screening
LDCT scans of
76 M and 413 Bnodules
= 0.88 in a leave-one-
out test
Iwano et al.
To classify nodules into
benign or malignant
LDA based on nodule's
circularity and second
moment features
HRCT (0.5–1 mm slice) scans of
52 Mand 55 B nodules
Sensitivity of 76.9% and
a specificity of 80%
Way et al.
To classify nodules into
benign or malignant
LDA or SVM with
stepwise feature selection
CT scans of 124 Mand
132 B nodules in 152 patients
= 0.857 in a leave-one-
out test
Chen et al.
To classify nodules into
benign or malignant
ANN ensemble
CT scans (slice thickness of 2.5
or 5 mm) of 19 M and
13B nodules
= 0.915 in a leave-one-
out test
Lee et al.
To classify nodules into
benign or malignant
GA-based feature
selection and a random
subspace method
Thick-slice (5 mm) CT scans of
62 M and 63 B nodules
= 0.889 in a leave-one-
out test
El-Baz et al.
To classify nodules into
benign or malignant
Analysis of the spatial
distribution of the nodule
Hounsfield values
CT scans (2 mm slice) of
51 M and 58 B nodules
Sensitivity of 92.3% and
a specificity of 96.6%
Classification between malignant (M) and benign (B) nodules based on shape and appearance features.
21
23. Image-Guided Radiotherapy
Gupta T, Narayan C A, Image-guided radiation therapy: Physician's perspectives, J Med Phys
Tumor Segmen
tation
Registration
Feature Extracti
on and Analysis
Registration
Registration
Tumor Segmen
tation
23
24. Radiotherapy and lung cancer
The efficacy and safety of RT reflect the interplay
between
• Total dose delivered to the malignant tumor
• The rate of dose delivery (daily fractionation)
• The volume (and type) of tumor-bearing organ irradiated.
• The intrinsic tolerance of the tissue irradiated
24
25. 4D CT
Excellent for taking into account
respiratory motion
Takes a set of CT images and
sorts them to represent each
phase of the breathing cycle
Box with infrared reflectors on
abdomen, set up infrared camera
to capture movement
26. Why is 4D CT important?
Same slice in different
phases of the breathing
cycle showing tumor
movement
26
27. Is 4DCT Worthwhile?
Underberg, R.W.M., Lagerwaard, F.J., Cuijpers, J.P., Slotman, B.J., Van Sornsen de Koste, J.R.,Senan, S. (2004). Four-Dimensional CT Scan
s for Treatment Planning in Stereotactic Radiotherapy for Stage 1 Lung Cancer, International Journal of Radiation Oncology Biology Physics 27
28. Gating
• Utilize 4DCT scan to get brea
thing pattern
• Determine a phase of the bre
athing cycle to treat during, p
lan on that scan Only
• Monitor treatment with respi
ratory motion
• when patient’s breathing
enters the selected part o
f the breathing cycle, tre
atment is delivered
Varian RPM system
28
29. Stereotactic body radiotherapy (SBRT)
Modeled after brain radiosurgery principles
• Multiple convergent beams
• Rigid patient immobilization
• Precise localization via stereotactic coordinate system
• Single fraction treatment
• Size-restriction for target
29
30. Anatomic Tumor Response Assessment in CT or MRI
Imaging as Surrogate for
Survival or progression-free survival
Response rate, time to tumor progression
RECIST criteria based on longest diameter
Complete response (CR): disappear
Partial response (PR): ≥ 50% decrease
Stable disease (SD): others
Progressive disease (PD): ≥ 25% increase or new
Tumor size change does not occur or does not occur early in
some effective treatments
30
31. Metabolic Tumor Response Assessment in FDG-PET
Strong correlation between FDG uptake and cancer cell
number
Metabolic (functional) change may occur earlier and more
markedly than tumor size change
Qualitative evaluation plus semi-quantitative assessment
with SUV or SUL
31
32. Wahl, J Nucl Med. 50(Suppl 1): 122S–150S.
Large decline in SUL (-41%) despite stable pancreatic mass anatomically (a
rrows) Partial metabolic response.
PET/CT for Tumor Response: An Example in Pancre
atic Tumor
32
33. Qualitative (Visual) PET Response Evaluation
Distribution and intensity of FDG uptake in tumor are
compared with uptake in normal tissues
Changes are visually evaluated
Requires clinical experience, disease patterns
Performs well in conversion of markedly positive PET scan to totally
negative scan
Moderate inter-observer variation
Difficult to detect small changes
33
34. Semi-Quantitative PET Response Assessment
SUV is most widely used
SUL is more consistent across patients
ROI selection
Maximal pixel: SUVmax, not as reproducible
Manual contour
Small fixed region ~1 cm3: SUVpeak
Fixed percentage isocontour: 40%, 50%
Fixed threshold: SUV = 2.5
3×SD above background (typically liver)
34
35. PERCIST Criteria
SULpeak of the hottest tumor
PERCIST criteria
CMR: normalize to background level
PMR: ≥ 30% decrease and ≥ 0.8 unit in SUL
SMR: others
PMD: ≥ 30% increase and ≥ 0.8 unit in SUL or visible increase in extent
of uptake (75% in TLG) with no decline in SUL, or new FDG-avid lesion
35
36. FDG Uptake Shows Spatial Variation
Belhassen and Zaidi 2010. Med Phys
Zhao, et al. 2005. J Nucl Med
36
37. Quantitative PET/CT analysis framework
Extract spatial-temporal image features:
Intensity distribution (histogram)
Spatial variations (texture)
Geometric properties (shape, structure)
Temporal changes due to therapy
Construct response models using machine learning
techniques with multiple features
Feature selection
Support vector machine
Cross-validation
37
38. • Region growing
• Morphology filter
• Multi-modality im
age segmentation
• ITK
• Intensity distribution
• Spatial variations
• Geometric properties
• > 100 features for each
tumor
• ITK
• ROC analyses
• Tumor response
• Survival
• Matlab
Tumor
Segmentation
Image
Registration
Feature
Extraction
Predicting
Ability
• Multi-level rigid
• Pre/Post-CT
• ITK
Extracting Spatial-Temporal FDG-PET Features for
Tumor Response Evaluation
38
39. Registration
Article Type of
Registration Abnormality Treatme
nt Scanning Time
Aristophanous
et al. Rigid NSCLC RT Before and after
treatment
Necib et al. Rigid Metastatic
colorectal cancer CTx Before and after
treatment
Tan et al. Rigid Esophageal Cancer CRT Before and after
treatment
Vera et al. Rigid Esophageal cancer CRT Before and during
treatment
Cannon et al. Deformable
(Demons)
Head and neck
cancer
RT or
CRT
Before and after
treatment
Roels et al. Deformable
(B-Spline) Rectal Cancer CRT Before, during and
after treatment
van Velden et al. Deformable
Advanced
colorectal
carcinoma
CTx Before and after
treatment
PET/CT based tumor response assessment studies using rigid registration, deformable registration or the combinatio
n of rigid and deformable registration algorithms. 39
40. Tumor Segmentation
Tumor segmentation can be performed either manually by
physicians or (semi-)automatically using image analysis
tools.
The accuracy of a tumor segmentation method is often hard
to evaluate in patients due to the lack of ground truth.
In response evaluation that involves two or more serial image
studies
The reproducibility of a segmentation method is as important as its
accuracy
40
41. Multi-modality adaptive region-growing (MARG)
A sharp volume increase occurred at an f where the region
just grows into the background (normal tissue)
f was identified by fitting the curve and calculating curvature
Tumor A
rea
Background Area
f
41
42. MARG: Results on a NSCLC Dataset from AAPM
TG211
Pathologic tumor v
olume
MARG results50% threshold res
ult
• For 10 patients, MARG (Dice = 0.69), slightly higher accuracy than thresh
olding methods (Dice = 0.67)
• Accuracy limited by the reliability of 3D pathologic tumor volume reconstr
uction and its alignment with PET/CT images 42
43. Spatial-Temporal FDG-PET Features for Predicting
Pathologic Tumor Response
A new SUV intensity feature - Skewness
pre-CRT
• Top: responder, more skewed (fewer hig
her SUVs)
• Bottom: non-responder, less skewed (mo
re higher SUVs)
Three texture features post-CRT – Inertia, Co
rrelation, and Cluster Prominence
• Top: responder, homogeneous FDG uptake p
ost-CRT
• Bottom: non-responder, heterogeneous FDG
uptake post-CRT
43
45. FDG-PET Histogram Distances for Predicting
Pathologic Tumor Response
• A responder shows larger histogram dista
nce from pre-CRT to post-CRT
• A non-responder shows smaller histogra
m distance
45
46. Accuracy of Individual Histogram Distances
7 bin-to-bin and 7 cross-bin histogram distances have high
er AUCs than conventional PET/CT response measures
46
48. Results
20 patients with esophageal cancer. Model predicts
pathologic response to chemoradiotherapy (CRT)
SVM model with 17 selected features from all feature groups:
AUC = 1.0, sensitivity = 100%, specificity = 100%
Models with conventional PET/CT response measures or
clinical parameters: AUCs < 0.75
48
49. Conclusions
Image processing in Lung cancer screening and
treatment
Computer aided detection and diagnosis
• Lung segmentation
• Nodule detection and segmentation
• Feature extraction and analysis
Image-guided radiotherapy
• Registration
– CT/CT, PET/CT
• Tumor segmentation
• Feature extraction and analysis
49
50. Future works
Validate the accuracy of image registration and tumor segmentation
methods
The usefulness of image features, and the generalizability of
response models
often developed on small retrospective datasets in large retrospective and
prospective datasets.
Clinic and biologic interpretation of the advanced PET/CT image
features
For physicians and biologists
Challenges for implementing the quantitative PET/CT image
analysis for tumor response evaluation
Delineating the tumor volume in multi-modality (PET/CT) images
Identifying a few features that truly capture biological changes correlated with
tumor response for a specific disease and therapy
Validating the results in large, multi-center patient datasets, vendor
implementation and ultimately clinic acceptance