AUTHOR=Hsu Li-Ming , Wang Shuai , Ranadive Paridhi , Ban Woomi , Chao Tzu-Hao Harry , Song Sheng , Cerri Domenic Hayden , Walton Lindsay R. , Broadwater Margaret A. , Lee Sung-Ho , Shen Dinggang , Shih Yen-Yu Ian TITLE=Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net JOURNAL=Frontiers in Neuroscience VOLUME=14 YEAR=2020 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.568614 DOI=10.3389/fnins.2020.568614 ISSN=1662-453X ABSTRACT=

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.