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16 March 2020 Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT
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Abstract
Low-dose thoracic CT (LDCT) screening has provided a low risk method of obtaining useful clinical information with lower quality images. Coronary artery calcium (CAC), a major indicator of cardiovascular disease, can be visualized on LDCT images. Additionally, the U-Net architecture has shown outstanding performance in a variety of medical imaging tasks, including image segmentation. Thus, the purpose of this study is to analyze the potential of the U-Net in the classification and localization of CAC in LDCT images. This study was performed with 814 LDCT cases with radiologist-determined CAC severity scores. A total of 3 truth masks per image were manually created for training of 3 U-Nets that were used to define the CAC search region, identify CAC candidates, and eliminate false positives (namely, aortic valve calcifications). Additionally, a single network tasked with only CAC candidate identification was tested to assess the need for different sections of the cascade of U-Nets. All CAC segmentation tasks were assessed using ROC analysis in the task of determining whether or not a case contained any CAC. The area under the ROC curve (AUC) as a performance metric and preliminary analysis showed potential for extension to a full classification task. CAC detection through the total cascade of 3 networks achieved and AUC of 0.97 +/- 0.01. Overall, this study shows significant promise in the localization and classification of CAC in LDCT images using a cascade of U-Nets.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jordan D. Fuhrman, Rowena Yip, Artit C. Jirapatnakul, Claudia I. Henschke, David F. Yankelevitz, and Maryellen L. Giger "Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140A (16 March 2020); https://doi.org/10.1117/12.2549117
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