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Abstract #3520

Brain Gray Matter Nuclei Segmentation on Quantitative Susceptibility Mapping using Convolutional Neural Network

Chao Chai1, Pengchong Qiao2, Bin Zhao2, Huiying Wang3, Guohua Liu2, Hong Wu2, E.Mark Haacke4, Wen Shen1, Xinchen Ye5, Zhiyang Liu2, and Shuang Xia1
1Department of Radiology, Tianjin First Central Hospital, Tianjin Medical Imaging Institute, School of Medicine, Nankai University, Tianjin, China, 2Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China, 3Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China, 4Department of Radiology, Wayne State University, Detroit, MI, United States, 5DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

This study focused on developing an automatic gray matter nuclei segmentation method. A 3D convolutional neural network based method was proposed, which adopted patches with different resolutions as input for segmentation. Experimental results showed much higher segmentation accuracy over the atlas-based method and other deep-learning-based methods in terms of both the similarity and the surface distance metrics. The segmentation results of the proposed method is also evaluated in terms of measurement accuracy, where the proposed method achieves the highest consistency with the manual delineations.

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