Abstract #3486

DL-BET - A deep learning based tool for automatic brain extraction from structural magnetic resonance images in mice.

Sabrina Gjerswold-Selleck¹, Nanyan Zhu^2,3,4, Haoran Sun¹, Dipika Sikka¹, Jie Shi¹, Chen Liu^3,4,5, Tal Nuriel^4,6, Scott A. Small^4,7,8, and Jia Guo^3,9

¹Biomedical Engineering, Columbia University, New York, NY, United States, ²Biological Science, Columbia University, New York, NY, United States, ³Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States, ⁴Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States, ⁵Electrical Engineering, Columbia University, New York, NY, United States, ⁶Department of Pathology and Cell Biology, Columbia University, New York, NY, United States, ⁷Gertrude H. Sergievsky Center, Columbia University, New York, NY, United States, ⁸Radiology, Columbia University, New York, NY, United States, ⁹Psychiatry, Columbia University, New York, NY, United States

Brain extraction plays an integral role in image processing pipelines in both human and small animal preclinical MRI studies. Due to lack of state-of-the-art tools for automated brain extraction in rodent research, this step is often performed semi-supervised with manual correction, making it prone to inconsistent results. Here, we perform a multi-model brain extraction study and present a semi-automated preprocessing workflow and deep neural network with a 3D Residual Attention U-Net architecture as the optimal network for automated skull-stripping in neuroimaging analysis pipelines, achieving a DICE score of 0.987 and accuracy of 99.7%.

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