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Abstract #3249

Deep Learning Approach for Lumbosacral Plexus Segmentation from Magnetic Resonance Neurography: Initial Study

Jian Wang1, Guohui Ruan2,3, Yingjie Mei4, Yanjun Chen1, Jialing Chen1, Yanqiu Feng2,3, and Xiaodong Zhang1
1Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China, 2Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, 3School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 4China International Center, Philips Healthcare, Guangzhou, China

Accurate regional segmentation of the Lumbosacral Plexus (LSP) on magnetic resonance neurography (MRN) images is a fundamental requirement before LSP related disorders diagnosis can be achieved. In this paper, we utilize U-Net to segment LSP trunk and branch from three-dimensional fast field echo(3D-FFE) with principle of selective excitation technique (Proset) images. The results show that a U-Net deep learning framework expresses highly performance and less time-consumption for LSP segmentation in patients with degenerative spinal diseases and healthy subjects.

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