Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net

Li Ming Hsu, Shuai Wang, Paridhi Ranadive, Woomi Ban, Tzu Hao Harry Chao, Sheng Song, Domenic Hayden Cerri, Lindsay R. Walton, Margaret A. Broadwater, Sung Ho Lee, Dinggang Shen, Yen Yu Ian Shih

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


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.

Original languageEnglish
Article number568614
JournalFrontiers in Neuroscience
Publication statusPublished - 2020 Oct 7

Bibliographical note

Publisher Copyright:
© Copyright © 2020 Hsu, Wang, Ranadive, Ban, Chao, Song, Cerri, Walton, Broadwater, Lee, Shen and Shih.


  • MRI
  • U-net
  • brain mask
  • mouse brain
  • rat brain
  • segmentation
  • skull stripping

ASJC Scopus subject areas

  • General Neuroscience


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