Abstract #3953

Automatic Mouse Skull-Stripping in fMRI Research via Deep Learning Using 3D Attention U-net

Guohui Ruan¹, Jiaming Liu¹, Ziqi An¹, Kaibin Wu², Wufan Chen¹, Ed X. Wu³, and Yanqiu Feng¹

¹Guangdong Provincial Key Laboratory of Medical Image Processing & Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, China, ²School of Biomedical Engineering, Southern Medical University, Guangzhou, China, Guangzhou, China, ³Department of Electrical and Electronic Engineering, Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, Hong Kong, China

Skull-stripping is an important preprocessing step in functional magnetic resonance imaging (fMRI) research. In fMRI research using rodents, skull-stripping is still manually implemented, and it is very time-consuming. To address this problem, a 3D Attention U-net was trained for automatically extracting mouse brain from fMRI time series. The experimental results demonstrate that the mouse brain can be effectively extracted by the proposed method, and the corresponding fMRI results agrees well with the results with manual skull-stripping.

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