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Abstract #0810

Deep Learning for Determination of Myometrial Invasion Depth and Automatic Lesion Identification Based on Endometrial Cancer MR Imaging

Yida Wang1, Yinqiao Yi1, Minhua Shen2, He Zhang2, Xu Yan3, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China, 3MR Scientific Marketing, Siemens Healthcare, Shanghai, China

We proposed an deep learning approach to locate lesion and evaluate the myometrial invasion (MI) depth automatically on magnetic resonance (MR) images. Firstly, we trained a detection model based on YOLOv3 to locate lesion area on endometrial cancer MR (ECM) images. Then, the detected lesion regions on both sagittal and coronal images were simultaneously fed into a classification model based on Resnet to identify MI depth. Precision-recall curve, receiver operating characteristic curve and confusion matrix were used to evaluate the performance of the proposed method. The proposed model achieved good and time-efficient performance.

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