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Unsupervised Domain Adaptation for Medical Image Segmentation by Disentanglement Learning and Self-Training.
- Source :
-
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2024 Jan; Vol. 43 (1), pp. 4-14. Date of Electronic Publication: 2024 Jan 02. - Publication Year :
- 2024
-
Abstract
- Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel UDA method (namely DLaST) for medical image segmentation via disentanglement learning and self-training. Disentanglement learning factorizes an image into domain-invariant anatomy and domain-specific modality components. To make the best of disentanglement learning, we propose a novel shape constraint to boost the adaptation performance. The self-training strategy further adaptively improves the segmentation performance of the model for the target domain through adversarial learning and pseudo label, which implicitly facilitates feature alignment in the anatomy space. Experimental results demonstrate that the proposed method outperforms the state-of-the-art UDA methods for medical image segmentation on three public datasets, i.e., a cardiac dataset, an abdominal dataset and a brain dataset. The code will be released soon.
- Subjects :
- Image Processing, Computer-Assisted
Brain diagnostic imaging
Heart
Subjects
Details
- Language :
- English
- ISSN :
- 1558-254X
- Volume :
- 43
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- IEEE transactions on medical imaging
- Publication Type :
- Academic Journal
- Accession number :
- 35853072
- Full Text :
- https://doi.org/10.1109/TMI.2022.3192303