ArticleJournal of applied clinical medical physics2025
A semi-supervised domain adaptation method with scale-aware and global-local fusion for abdominal multi-organ segmentation.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A semi-supervised domain adaptation method with scale-aware and global-local fusion for abdominal multi-organ segmentation.Journal of applied clinical medical physics · 2025Article
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Abstract
backgroundAbdominal multi-organ segmentation remains a challenging task. Semi-supervised domain adaptation (SSDA) has emerged as an innovative solution. However, SSDA frameworks based on UNet struggle to capture multi-scale and global information. PURPOSE: Our work aimed to propose a novel SSDA method to achieve more accurate abdominal multi-organ segmentation with limited labeled target domain data, which has a superior ability to capture the multi-scale features and integrate local and global information effectively.
methodsThe proposed network is based on UNet. In the encoder part, a scale-aware with domain-specific batch normalization (SAD) module is integrated to adaptively extract multi-scale features and to get better generalization across source and target domains. In the bottleneck part, a global-local fusion (GLF) module is utilized for capturing and integrating both local and global information. They are integrated into the framework of self-ensembling mean-teacher (SE-MT) to enhance the model's capability to learn common features across source and target domains.
resultsTo validate the performance of the proposed model, we evaluated it on the public CHAOS and BTCV datasets. For CHAOS, the proposed method obtains an average DSC of 88.97% and ASD of 1.12 mm with only 20% labeled target data. For BTCV, it achieves an average DSC of 88.95% and ASD of 1.13 mm with 20% labeled target data. Compared with the state-of-the-art methods, DSC and ASD increased by at least 0.72% and 0.33 mm on CHAOS, 1.29% and 0.06 mm on BTCV, respectively. Ablation studies were also conducted to verify the contribution of each component of the model. The proposed method achieves a DSC improvement of 3.17% over the baseline with 20% labeled target data.
conclusionThe proposed SSDA method for abdominal multi-organ segmentation has a powerful ability to extract multi-scale and more global features, significantly improving segmentation accuracy and robustness.
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