Abstract #2036

Automatic Couinaud segmentation in Intravenous-Phase Enhanced MRI images by key point detection with deep neural network

Dong Miao^1,2, Xue Ren^3,4, Ying Zhao^3,4, AiLian Liu^3,4, Yu Yao^1,2, Qihao Xu^3,4, and Qingwei Song^3,4

¹Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China, ²School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China, ³Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China, ⁴Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian, China

Synopsis

Keywords: Liver, Liver, Couinaud segmentation

This study presents an effective framework for automatic Couinaud liver segmentation in Intravenous-Phase Enhanced MRI images, without the time-consuming delineation of each segment or identifying different branches of the vessel system. We define seven key points located at the bifurcations of the vascular system to divide liver into Couinaud segments II-VIII. We train a key point detection model to locate the coordinate of key point. The overall dice score is 81.17% and average surface distance is 2.35mm in test set.

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