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Abstract #3179

Deep Learning Algorithm for Automated Liver Segmentation Using Portal Venous Phase Magnetic Resonance Images

Xinjun Han1, Niange Yu2, Qianjiang Xiao2, Mingyang Gao2, Dandan Zheng2, and Zhenghan Yang1
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China, 2Shukun (Beijing) Technology Co., Ltd, Beijing, China


Accurate segmentation of liver not only facilitates the subsequent quantitative assessment of the regions of interest but also benefits precise diagnosis, and surgical planning. These tasks are usually performed by radiologists via visual inspection and manual delineations, which are tedious, labor-intensive, time-consuming. Convolutional neural networks (CNNs) have shown promise for performing automated liver segmentation for CT examinations, but there is less research on MR images. In this study, we provide a 3D U-Net based model for robust whole-liver and Couinaud segment measurements to support the treatment decision-making process on MR images.

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