Abstract #2181

Learning-based Segmentation for Monkey Brain MRI

Cuijin Lao^1,2, Jiawei Chen², Li Wang², Gang Li², and Dinggang Shen²

¹Department of Information Engineering, Liuzhou City Vocational College, Liuzhou, People's Republic of China, ²Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Accurate segmentation of monkey brain MRI is of great importance in studying the brain development, pathogenesis and progression of neurological diseases. However, it is challenging for automatic segmentation due to noise, low contrast and partial volume effect. Existing tools fine-tuned to human brain MRI are ineffective for monkey brain MRI due to their difference from human brain MRI. In this study, we propose a machine learning-based framework for the segmentation of monkey brain MRI into skull, cerebellum, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) of the cerebrum. The experiment results demonstrate that our proposed method outperforms than previous methods.

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