Abstract #1301

Accurate Brain Extraction Using 3D U-Net with Encoded Spatial Information

Hualei Shen¹, Chenyu Wang^1,2, Kain Kyle², Chun-Chien Shieh^2,3, Lynette Masters⁴, Fernando Calamante^1,5, Dacheng Tao⁶, and Michael Barnett^1,2

¹Brain and Mind Centre, the University of Sydney, Sydney, Australia, ²Sydney Neuroimaging Analysis Centre, Sydney, Australia, ³Sydney Medical School, the University of Sydney, Sydney, Australia, ⁴I-MED Radiology Network, Sydney, Australia, ⁵Sydney Imaging and School of Biomedical Engineering, the University of Sydney, Sydney, Australia, ⁶School of Computer Science, the University of Sydney, Sydney, Australia

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 minutes and has been deployed for routine image analyses at the Sydney Neuroimaging Analysis Centre.

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