Computer-aided Detection of Pulmonary Nodules using Genetic Programming
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This document describes a study that used genetic programming to develop an accurate classifier for detecting pulmonary nodules on CT scans. The proposed method involved segmenting the lungs, detecting nodule candidates, extracting features, and using genetic programming to evolve a combination of features and functions to classify nodules versus non-nodules. When tested on 153 nodules across 32 CT scans, the genetic programming classifier achieved a sensitivity of 92% and specificity of 86%.
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Abstract
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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).
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Abstract
Purpose/Objective(s)
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Materials/Methods
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Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
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As the lung cancer is the leading cause of cancer
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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.
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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.
Radiomics and Deep Learning for Lung Cancer Screening
The document summarizes research on using radiomics and deep learning approaches for lung cancer screening. It describes:
1) Using radiomic features like shape, texture, and intensity from lung nodules on CT scans and an SVM-LASSO model to classify nodules with 87.9% sensitivity and 78.2% specificity, outperforming the Lung-RADS system.
2) A deep learning model developed for a Kaggle competition that achieved 67.4% accuracy on nodule classification but only ranked 99th due to overfitting issues without enough data.
3) Future work could integrate quantification of nodule characteristics like spiculation with plasma biomarkers to improve diagnostic accuracy.
Lung Nodule detection System
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
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.
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
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.
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
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.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
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.
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...
As the lung cancer is the leading cause of cancer
death in the medical field, Computed Tomography (CT) scan
of the thorax is widely applied in diagnoses for identifying
the lung cancer. In this paper, a technique of rotation invariant
with Local Binary Pattern (LBP) for segmentation of various
lung nodules from the Lung CT cancer data sets is used. This
is tested on various lung data sets from teaching files of
Casimage database and National Cancer Institute (NCI) of
National Biomedical Imaging Archive (NBIA). The results
show the segmented nodules with clear boundaries, which is
helpful in diagnosis of lung cancer. Further, the results are
compared with the watershed segmentation method, which
shows that LBP based method yields better segmentation
accuracy.
Automatic detection of lung cancer in ct images
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.
LIVER CANCER DETECTION USING CT/(MRI) IMAGES
K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE BASED LIVER CANCER DETECTION USING CT/(MRI) MAGNETIC RESONANCE IMAGES
Image registration and data fusion techniques.pptx latest save
Medical imaging is the fundamental tool in conformal radiation therapy. Almost every aspect of patient management involves some form of two or three dimensional image data acquired using one or more modality.
Image data are now used for diagnosis and staging, for treatment planning and delivery and for monitoring patients after therapy.
Hsc 340 9 16
The document summarizes the key steps in conventional and CT simulation procedures for radiation therapy treatment planning. These include diagnosis and staging of the cancer, therapeutic decisions, patient simulation using fluoroscopy or CT scanning for positioning and immobilization, treatment planning to identify target volumes and organs at risk, treatment delivery and monitoring, and patient follow-up. CT simulation allows for better visualization of anatomy and automatic contouring compared to conventional simulation. Both methods require accurate documentation of the simulation process.
Multi modality medical image registration
Medical image fusion combines information from different imaging modalities like PET, CT, MRI into a single image. It has revolutionized medical diagnosis in various areas like oncology, brain imaging, and cardiology. Hybrid imaging using external markers or software registration are common fusion approaches. PET/CT fusion provides improved anatomical localization and cancer staging by combining metabolic PET information with anatomical CT data. New applications of fusion include image-guided interventions using ultrasound and CT fusion.
Technical Advances in radiotherapy for Lung (and liver) Cancer
This document summarizes recent technical advances in radiotherapy for lung and liver cancer, including: 4DCT imaging to account for tumor motion; motion management techniques like gating and breath-holding; intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) to improve dose conformity; image-guided radiation therapy (IGRT) to reduce margins and enable adaptations; and proton therapy which may further reduce normal tissue dose due to its physical properties, though proton techniques are still evolving to address motion and anatomical changes. The document outlines the benefits and challenges of each technique through examples and studies.
Innovations conference 2014 dr shalini vinod dedicated magnetic resonance i...
This document describes the implementation of an MRI simulator at Liverpool Hospital for radiotherapy planning. Key points:
- An MRI simulator was installed in June 2013 to allow MRI-based radiotherapy planning for common cancer sites like head and neck, cervix, prostate, rectum, and brain.
- Dedicated protocols were developed with input from tumor-specific groups to optimize visualization of tumors, lymph nodes, and normal tissues.
- Over 200 patients have been simulated on the MRI simulator from August 2013 to June 2014 for various cancer sites.
- The MRI simulator allows improved delineation of targets and organs-at-risk compared to CT and can provide functional imaging data to guide treatment planning.
Current and emerging partnerships in imaging and radiotherapy by musila mutala
This document discusses the role of imaging in radiotherapy and outlines several key points:
1. Imaging has evolved from basic x-rays to cross-sectional imaging like CT and MRI to better define tumor volumes and organs at risk.
2. Modern radiotherapy relies on accurate tumor delineation using imaging for treatment planning, simulation, delivery, verification and monitoring.
3. Emerging technologies like PET and functional MRI provide complementary anatomical and biological information that can further improve radiotherapy outcomes.
4. Greater collaboration between radiology and radiation oncology departments, including dedicated imaging units and personnel, can help maximize the benefits of modern imaging for radiotherapy.
What's hot (20)
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Optimal fuzzy rule based pulmonary nodule detection
Optimal fuzzy rule based pulmonary nodule detection
Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular...
Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular...
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Radiomics and Deep Learning for Lung Cancer Screening
Radiomics and Deep Learning for Lung Cancer Screening
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...
Image registration and data fusion techniques.pptx latest save
Image registration and data fusion techniques.pptx latest save
Technical Advances in radiotherapy for Lung (and liver) Cancer
Technical Advances in radiotherapy for Lung (and liver) Cancer
Innovations conference 2014 dr shalini vinod dedicated magnetic resonance i...
Innovations conference 2014 dr shalini vinod dedicated magnetic resonance i...
Current and emerging partnerships in imaging and radiotherapy by musila mutala
Current and emerging partnerships in imaging and radiotherapy by musila mutala
Similar to Computer-aided Detection of Pulmonary Nodules using Genetic Programming
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
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.
Interpretable Spiculation Quantification for Lung Cancer Screening
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.
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resour...
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resour...Anjani Dhrangadhariya
PICO recognition is an information extraction task for identifying participant, intervention, comparator, and outcome information from clinical literature.
Manually identifying PICO information is the most time-consuming step for conducting systematic reviews (SR) which is already a labor-intensive process.
A lack of diversified and large, annotated corpora restricts innovation and adoption of automated PICO recognition systems.
The largest-available PICO entity/span corpus is manually annotated which is too expensive for a majority of the scientific community.
To break through the bottleneck, we propose DISTANT-CTO, a novel distantly supervised PICO entity extraction approach using the clinical trials literature, to generate a massive weakly-labeled dataset with more than a million ``Intervention'' and ``Comparator'' entity annotations.
We train distant NER (named-entity recognition) models using this weakly-labeled dataset and demonstrate that it outperforms even the sophisticated models trained on the manually annotated dataset with a 2\% F1 improvement over the Intervention entity of the PICO benchmark and more than 5\% improvement when combined with the manually annotated dataset.
We investigate the generalizability of our approach and gain an impressive F1 score on another domain-specific PICO benchmark.
The approach is not only zero-cost but is also scalable for a constant stream of PICO entity annotations.Lung nodule diagnosis from CT images based on ensemble learning
Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.
Iaetsd classification of lung tumour using
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
Image Guided Radiotherapy
This document discusses image-guided radiation therapy (IGRT) and its evolution and applications. It begins by defining IGRT as external beam radiation therapy using imaging prior to each treatment fraction to verify patient positioning. IGRT allows for reduction of safety margins by compensating for set-up errors and organ motion. The document then reviews the history of IGRT from early portal imaging to modern cone-beam CT and other volumetric imaging techniques. It provides examples of IGRT protocols and clinical outcomes for sites such as prostate, lung, liver, and central nervous system tumors.
Cervical Spine Range of Motion Measurement Utilizing Image Analysis - VISAPP2022
This study developed a system to automatically measure cervical spine range of motion (CRoM) angles from cervical spine X-ray images using deep learning. The system used Mask R-CNN for image segmentation and measured angles between vertebrae similarly to manual methods. An evaluation found the average error was 3.5 degrees with a standard deviation of 2.8 degrees, comparable to measurements by residents. However, accuracy was poorer for the C1/C2 vertebrae. Future work will explore improving segmentation and developing computer-aided diagnosis of cervical issues.
Artificial Intelligence in Radiation Oncology
The document discusses artificial intelligence applications in radiation oncology. It begins with acknowledgements and then outlines topics including radiomics decision support tools, automatic delineation and variability analysis, and applications like lung cancer screening, tumor response prediction, and aggressive lung adenocarcinoma subtype prediction. Radiomics frameworks and deep learning models are presented. Results show potential for AI to provide quantitative imaging biomarkers and improve outcomes in areas like screening, treatment planning, and response assessment.
14 00-20171207 rance-piv_c
Gestion des données scientifiques en imagerie in vivo – Journée scientifique organisée par PIV le 7 décembre 2017 au PARCC-HEGP
Bastien RANCE
HEGP
BELLOTTI_PHYxHEA_final.ppt
The MAGIC-5 lung CAD system uses three parallel approaches - region growing, virtual ants, and voxel-based neural networks - to detect and classify lung nodules in CT scans. It was validated on three databases and performs competitively with state-of-the-art systems. In the ANODE09 international competition, the three MAGIC-5 CADs achieved scores between 0.25-0.29 for detecting pulmonary nodules, outperforming other participating CAD systems. The MAGIC-5 project aims to support medical diagnosis and allow large-scale image analysis through developing models and algorithms for biomedical image analysis.
Kshivets Hong Kong Sydney2020
Combined Lobectomies and Pneumonectomies: Survival Outcomes in Patients with Local Advanced Lung Cancer
Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy
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)
极之聚地产推广部培训.PPT
This document discusses the use of GEANT4 and the GATE toolkit for simulating emission tomography systems like PET and SPECT. It provides an overview of why Monte Carlo simulations are useful for imaging, the architecture of GATE which is based on GEANT4, and how GATE handles geometry, time, physics processes, digitization and more. It also provides examples of how GATE has been validated against real patient and preclinical imaging systems and discusses its potential applications and current limitations like long simulation times.
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...Aboul Ella Hassanien
The document proposes a hybrid method using neutrosophic sets and fuzzy c-means clustering to improve liver segmentation in CT images. It transforms the image into neutrosophic domains of truth, indeterminacy, and falsity. Thresholds are adapted using fuzzy c-means to binarize the domains. Experimental results on 30 abdominal CT images found 88% accuracy by Jaccard index and 94% by Dice coefficient, outperforming other methods. The approach effectively handles noise and uncertainty to produce clear liver boundaries.Comparison of a portal image with reference images (simulation films or digit...
1) The study evaluated setup errors in radiotherapy patients by comparing portal images to reference images (digitally reconstructed radiographs or simulation films).
2) Setup errors were analyzed in two orthogonal directions for anterior and lateral fields in 43 patients receiving pelvic, prostate, abdominal or thoracic radiotherapy.
3) Transfer errors between the planning and treatment positions ranged from 0.3-2.8mm depending on the reference image and treatment site. Using digitally reconstructed radiographs as references resulted in slightly smaller transfer errors compared to simulation films.
Introduction to compressed sensing MRI
This presentation is a starter for folks interested in the implementation and application of compressed sensing (CS) MRI. It includes a Matlab demo and list of well-known resources for CS MRI.
morphometry.pptx
Morphometry involves obtaining quantitative information about biological tissue structures through measurements of their size, shape, and other metrics. It provides objective criteria that can improve reproducibility in histopathology. Key applications include tumor classification, diagnostic accuracy, and distinguishing conditions like hyperplasia vs carcinoma. Techniques include planimetry, point counting, pixel counting in digital images. Morphometry of features like nuclear size and shape correlates with biological behaviors and can aid diagnosis of conditions like endometrial hyperplasia vs cancer or malignant mesothelioma. Next-generation techniques continue advancing this quantitative approach.
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...
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.
Random and systematic errors 25.10.12
This document provides definitions and examples of random and systematic errors that can occur during the radiotherapy treatment process. It discusses various sources of errors including patient setup, organ motion, and target deformation. Methods for managing errors such as offline and online correction techniques, immobilization devices, and image-guidance are presented. The importance of distinguishing between random and systematic errors when establishing appropriate planning target volume margins is also emphasized.
Similar to Computer-aided Detection of Pulmonary Nodules using Genetic Programming (20)
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
Interpretable Spiculation Quantification for Lung Cancer Screening
Interpretable Spiculation Quantification for Lung Cancer Screening
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resour...
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resour...
Lung nodule diagnosis from CT images based on ensemble learning
Lung nodule diagnosis from CT images based on ensemble learning
Cervical Spine Range of Motion Measurement Utilizing Image Analysis - VISAPP2022
Cervical Spine Range of Motion Measurement Utilizing Image Analysis - VISAPP2022
Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy
Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...
Comparison of a portal image with reference images (simulation films or digit...
Comparison of a portal image with reference images (simulation films or digit...
Second Review Research in quantifying lymphocytes.pptx
Second Review Research in quantifying lymphocytes.pptx
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...
More from Wookjin Choi
Deep Learning-based Histological Segmentation Differentiates Cavitation Patte...
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...
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 Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation...
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.
CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Ra...
The CIRDataset provides a large-scale dataset of 956 annotated lung nodules with segmentations and classifications of spiculations and lobulations, which are important radiomic features for assessing malignancy. It aims to address the lack of publicly available datasets capturing these subtle radiological features typically assessed by radiologists but often smoothed over by deep learning segmentation models. The dataset is accompanied by code, models, and a pipeline to enable the development of AI systems for joint nodule segmentation, classification of spiculations/lobulations, and malignancy prediction using an end-to-end deep learning approach.
Artificial Intelligence in Radiation Oncology.pptx
The document discusses artificial intelligence applications in radiation oncology, including automatic delineation of organs-at-risk using deep learning models like OARNet. It also discusses radiomics approaches for clinical decision support and outcomes prediction using features extracted from medical images with techniques like spiculation quantification for lung cancer screening.
Artificial Intelligence in Radiation Oncology
This document discusses artificial intelligence applications in radiation oncology. It begins with acknowledgements and then outlines several AI applications including radiomics tools for lung cancer screening, tumor response prediction, and predicting aggressive lung adenocarcinoma subtypes. It also discusses using AI for automatic tumor delineation and quantification of delineation variability as well as local tumor morphological changes prediction and metabolic tumor volume changes. The document provides details on methods and results for several of these AI applications in radiation oncology.
Artificial Intelligence in Radiation Oncology
- The document discusses the use of artificial intelligence and radiomics in radiation oncology. It presents frameworks for radiomics analysis involving image registration, tumor segmentation, feature extraction, and predictive modeling.
- Specific applications discussed include using radiomics for lung cancer screening and prediction of tumor response. Radiomics features combined with machine learning models show improved performance over clinical guidelines for assessing lung nodule malignancy.
- Methods are also presented for quantifying tumor characteristics like spiculation through image analysis and extracting interpretable radiomics features. This can provide semantic information to radiologists for assessment.
Automatic motion tracking system for analysis of insect behavior
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 ...
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.
Quantitative Cancer Image Analysis
Delineation Variability
- Human Delineation Variability Quantification
- Realistic Variability Simulation
- Probabilistic U-Net
Radiomics
- Lung Cancer Screening
- Spiculation Quantification
- Aggressive Lung ADC subtype prediction
Simulation of Realistic Organ-At-Risk Delineation Variability in Head and Nec...
(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 therapy
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 Screening
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.
Radiomics in Lung Cancer
Radiomics
1. Lung Cancer Screening
2. Spiculation Quantification
3. Aggressive Lung ADC Subtype
Dual energy CT in radiotherapy: Current applications and future outlook
This document summarizes a review article on the current and future applications of dual energy CT (DECT) in radiation therapy. It describes how DECT can be used to estimate electron density, decompose tissue into effective atomic numbers, and quantify contrast material for improved dose calculations, tissue characterization, and treatment planning. Several clinical applications are discussed, including more accurate dose calculations for brachytherapy and proton therapy, metal artifact reduction, and normal tissue assessment. The document concludes that DECT has the potential to improve accuracy at various stages of the radiotherapy workflow and will likely be used more in the future to provide additional diagnostic information over single energy CT.
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Deep Learning-based Histological Segmentation Differentiates Cavitation Patte...
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