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15 February 2025 |
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Article overview
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Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning | Xiaoyang Chen
; Jinjian Wu
; Wenjiao Lyu
; Yicheng Zou
; Kim-Han Thung
; Siyuan Liu
; Ye Wu
; Sahar Ahmad
; Pew-Thian Yap
; | Date: |
3 Jan 2023 | Abstract: | Automatic segmentation of brain MR images into white matter (WM), gray matter
(GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis
and cortical surface reconstruction. Due to dramatic structural and appearance
changes associated with developmental and aging processes, existing brain
tissue segmentation methods are only viable for specific age groups.
Consequently, methods developed for one age group may fail for another. In this
paper, we make the first attempt to segment brain tissues across the entire
human lifespan (0-100 years of age) using a unified deep learning model. To
overcome the challenges related to structural variability underpinned by
biological processes, intensity inhomogeneity, motion artifacts,
scanner-induced differences, and acquisition protocols, we propose to use
contrastive learning to improve the quality of feature representations in a
latent space for effective lifespan tissue segmentation. We compared our
approach with commonly used segmentation methods on a large-scale dataset of
2,464 MR images. Experimental results show that our model accurately segments
brain tissues across the lifespan and outperforms existing methods. | Source: | arXiv, 2301.01369 | Services: | Forum | Review | PDF | Favorites |
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