Accurate brain extraction using 3D U-Net with encoded spatial information
Brain extraction from 3D MRI datasets using existing 3D U-Net convolutional neural
networks suffers from limited accuracy. Our proposed method overcame this challenge by
combining a 3D U-Net with voxel-wise spatial information. The model was trained with 1,615
T1 volumes and tested on another 601 T1 volumes, both with expertly segmented labels.
Results indicated that our method significantly improved the accuracy of brain extraction
over a conventional 3D U-Net. The trained model extracts the brain from a T1 volume in~ 2 …
networks suffers from limited accuracy. Our proposed method overcame this challenge by
combining a 3D U-Net with voxel-wise spatial information. The model was trained with 1,615
T1 volumes and tested on another 601 T1 volumes, both with expertly segmented labels.
Results indicated that our method significantly improved the accuracy of brain extraction
over a conventional 3D U-Net. The trained model extracts the brain from a T1 volume in~ 2 …
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