| First Author | Hsu LM | Year | 2020 |
| Journal | Front Neurosci | Volume | 14 |
| Pages | 568614 | PubMed ID | 33117118 |
| Mgi Jnum | J:353231 | Mgi Id | MGI:6728595 |
| Doi | 10.3389/fnins.2020.568614 | Citation | Hsu LM, et al. (2020) Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net. Front Neurosci 14:568614 |
| abstractText | 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. |
