Automated Brain Extraction from T1-weighted MRI of Rhesus Macaques using a U-Net Deep Learning Framework
Anqi Zhang1, Donghoon Kim1,2, Jeong-chul Kim3, Brad A. Hobson1, Sonny R. Elizaldi4, John Morrison5, Smita Iyer4, Abhijit J. Chaudhari2,4, and Youngkyoo Jung1,2,3
1Biomedical Engineering, University of California, Davis, CA, United States, 2Radiology, University of California, Davis, CA, United States, 3Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States, 4California National Primate Research Center, University of California, Davis, CA, United States, 5Neurology, University of California, Davis, CA, United States
Transfer learning of an advanced deep learning framework, utilizing 3D U-Net pre-trained model, and trained on human brain MRI scans is proposed for brain extraction from MRI of other species. The proposed network architecture successfully performed whole brain extraction from rhesus macaque brain MRIs automatically with high accuracy, reduced errors, and lower computational cost. Options for data augmentation and different learning rates were also tested. Successful implementation of automated brain extraction would offer the potential to apply the same strategy on other animal models.
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