TY - GEN
T1 - Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT
AU - Fuhrman, Jordan D.
AU - Yip, Rowena
AU - Jirapatnakul, Artit C.
AU - Henschke, Claudia I.
AU - Yankelevitz, David F.
AU - Giger, Maryellen L.
N1 - Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085517264&partnerID=8YFLogxK
U2 - 10.1117/12.2549117
DO - 10.1117/12.2549117
M3 - Conference contribution
AN - SCOPUS:85085517264
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Hahn, Horst K.
A2 - Mazurowski, Maciej A.
PB - SPIE
T2 - Medical Imaging 2020: Computer-Aided Diagnosis
Y2 - 16 February 2020 through 19 February 2020
ER -