Abstract #0762

Deep Learning for the Ovarian Lesion Localization and Discrimination Between Borderline Tumors and Cancers in MR Imaging

Yida Wang¹, YinQiao Yi¹, Haijie Wang¹, Changan Chen², Yingfang Wang², Guofu Zhang², He Zhang², and Guang Yang¹

¹East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China, ²Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China

We proposed a deep learning (DL) approach to segment ovarian lesion and differentiate ovarian malignant from borderline tumors in MR Imaging. Firstly, we used U-net++ with deep supervision to automatically define lesion region on conventional MRI; secondly, the segmented ovarian masses regions were classified with an SE-ResNet model. We compared the performance of classification model with those of radiologist’. The results showed the trained DL network model could help to identify and categorize ovarian masses with a high accuracy from MR images.

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