Abstract #3249

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

Jian Wang¹, Guohui Ruan^2,3, Yingjie Mei⁴, Yanjun Chen¹, Jialing Chen¹, Yanqiu Feng^2,3, 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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