Name
Title | ||
|---|---|---|
DDA-Net: Unsupervised cross-modality medical image segmentation via dual domain adaptation |
Abstract | ||
|---|---|---|
Background and Objective: Deep convolutional networks are powerful tools for single-modality medical image segmentation, whereas generally require semantic labelling or annotation that is laborious and time-consuming. However, domain shift among various modalities critically deteriorates the performance of deep convolutional networks if only trained by single-modality labelling data. Methods: In this paper, we propose an end-to-end unsupervised cross-modality segmentation network, DDA-Net, for accurate medical image segmentation without semantic annotation or labelling on the target domain. To close the domain gap, different images with domain shift are mapped into a shared domain-invariant representation space. In addition, spatial position information, which benefits the spatial structure consistency for semantic information, is preserved by an introduced cross-modality auto-encoder. Results: We validated the proposed DDA-Net method on cross-modality medical image datasets of brain images and heart images. The experimental results show that DDA-Net effectively alleviates domain shift and suppresses model degradation. Conclusions: The proposed DDA-Net successfully closes the domain gap between different modalities of medical image, and achieves state-of-the-art performance in cross-modality medical image segmentation. It also can be generalized for other semi-supervised or unsupervised segmentation tasks in some other field. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cmpb.2021.106531 | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE |
Keywords | DocType | Volume |
Cross-modality, Medical image, Segmentation, Domain adaptation, Unsupervised learning | Journal | 213 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuesheng Bian | 1 | 3 | 4.14 |
| Xiongbiao Luo | 2 | 124 | 22.22 |
| Cheng Wang | 3 | 218 | 32.63 |
| Weiquan Liu | 4 | 0 | 0.34 |
| Xiuhong Lin | 5 | 3 | 3.81 |