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U-net combined with CRF and anatomical based spatial features to segment white matter hyperintensities

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收录情况： ◇ CPCI(ISTP) ◇ EI

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机构： [1]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China. [2]Peng Cheng Laboratory, Shenzhen, Guangdong, China [3]Mindsgo Life Science Shenzhen Ltd, ShenZhen, Guangdong, China. [4]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China. [5]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.

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White matter hyperintensities (WMH) are important biomarkers for cerebral small vessel disease and closely associated with other neurodegenerative process. In this paper, we proposed a fully automatic WMH segmentation method based on U-net architecture. CRF were combined with U-net to refine segmentation results. We used a new anatomical based spatial feature produced by brain tissue segmentation based on T1 image, along with intensities of T1 and T2-FLAIR images to train our neural network. We compared 8 forms of automated WMH segmentation methods, range from traditional statistical learnng methods to deep learning based methods, with different architecture and used different features. Results showed our proposed method achieved best performance in terms of most metrics, and the inclusion of anatomical based spatial features strongly increase the segmentation performance.

基金：

基金编号： 2018YFC1312000 61801145 JCYJ20180306171800589

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第一作者机构： [1]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China. [2]Peng Cheng Laboratory, Shenzhen, Guangdong, China

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