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Abstract #3486

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

Sabrina Gjerswold-Selleck1, Nanyan Zhu2,3,4, Haoran Sun1, Dipika Sikka1, Jie Shi1, Chen Liu3,4,5, Tal Nuriel4,6, Scott A. Small4,7,8, and Jia Guo3,9
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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