ArticleMachine learning in medical imaging. MLMI (Workshop)2020
Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation.
Article in Machine learning in medical imaging. MLMI (Workshop), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Weakly Supervised Cerebellar Cortical Surface Parcellation with Self-Visual Representation Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Deformable Spherical Transformer for Cerebellar Surface Parcellation.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts.Machine learning in medical imaging. MLMI (Workshop) · 2021Article
- Harmonized neonatal brain MR image segmentation model for cross-site datasets.Biomedical signal processing and control · 2021Article
- Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge.IEEE transactions on medical imaging · 2021Review
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Authors and funding
7 authors.
Funding
Abstract
To characterize early cerebellum development, accurate segmentation of the cerebellum into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) tissues is one of the most pivotal steps. However, due to the weak tissue contrast, extremely folded tiny structures, and severe partial volume effect, infant cerebellum tissue segmentation is especially challenging, and the manual labels are hard to obtain and correct for learning-based methods. To the best of our knowledge, there is no work on the cerebellum segmentation for infant subjects less than 24 months of age. In this work, we develop a semi-supervised transfer learning framework guided by a confidence map for tissue segmentation of cerebellum MR images from 24-month-old to 6-month-old infants. Note that only 24-month-old subjects have reliable manual labels for training, due to their high tissue contrast. Through the proposed semi-supervised transfer learning, the labels from 24-month-old subjects are gradually propagated to the 18-, 12-, and 6-month-old subjects, which have a low tissue contrast. Comparison with the state-of-the-art methods demonstrates the superior performance of the proposed method, especially for 6-month-old subjects.
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