Generalizing deep learning brain segmentation for skull removal and intracranial measurements
SCIE
SCOPUS
- Title
- Generalizing deep learning brain segmentation for skull removal and intracranial measurements
- Authors
- Liu, Yue; Huo, Yuankai; Dewey, Blake; Wei, Ying; Lyu, Ilwoo; Landman, Bennett A.
- Date Issued
- 2022-05
- Publisher
- Elsevier BV
- Abstract
- Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonance imaging (MRI). Detailed whole brain segmentation provides a noninvasive way to measure brain regions. Furthermore, increasing neuroimaging data are distributed in a skull stripped manner for privacy protection. Therefore, generalizing deep learning brain segmentation for skull removal and intracranial measurements is an appealing task. However, data availability is challenging due to a limited set of manually traced atlases with whole brain and TICV/PFV labels. In this paper, we employ U-Net tiles to achieve automatic TICV estimation and whole brain segmentation simultaneously on brains w/and w/o the skull. To overcome the scarcity of manually traced whole brain volumes, a transfer learning method is introduced to estimate additional TICV and PFV labels during whole brain segmentation in T1-weighted MRI. Specifically, U-Net tiles are first pre-trained using large-scale BrainCOLOR atlases without TICV and PFV labels, which are created by multi-atlas segmentation. Then the pre-trained models are refined by training the additional TICV and PFV labels using limited BrainCOLOR atlases. We also extend our method to handle skull-stripped brain MR images. From the results, our method provides promising whole brain segmentation and volume estimation results for both brains w/and w/o skull in terms of mean Dice similarity coefficients and mean surface distance and absolute volume similarity. This method has been made available in open source (https://github. com/MASILab/SLANTbrainSeg_skullstripped).
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/120865
- DOI
- 10.1016/j.mri.2022.01.004
- ISSN
- 0730-725X
- Article Type
- Article
- Citation
- Magnetic Resonance Imaging, vol. 88, page. 44 - 52, 2022-05
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