RefineCatDiff: Toward High‐Quality Medical Image Segmentation via a Categorical Diffusion Refinement Framework Article Swipe
Feng Liu
,
Hongjian Yu
,
Jiaan Huang
,
Xin Hua
,
H. J. Liu
,
Zhanchao Ma
,
Zhen Wang
,
Zhijiang Du
,
Lining Sun
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/aisy.202401125
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/aisy.202401125
High‐quality medical image segmentation is crucial for clinical applications. However, existing convolutional neural network‐based or vision transformer‐based segmentation models often struggle to produce accurate masks when dealing with complex images. In contrast, diffusion models are particularly effective at capturing fine‐grained features in images, making them well‐suited for these challenges. Therefore, RefineCatDiff, a refinement framework that leverages the strengths of diffusion models to achieve high‐quality medical image segmentation, is proposed. In this framework, an initial‐stage model predicts a coarse mask, which is subsequently refined by a diffusion model to generate a more accurate and detailed mask. Specifically, a categorical distribution‐based discrete diffusion model is developed for refinement, which better aligns with the characteristics of segmentation tasks, thereby enhancing its effectiveness. Moreover, the coarse mask is incorporated as prior knowledge into the diffusion process to further improve efficiency. Additionally, the Prior Fusion Guidance Module, the Encoder Coupled Feature Fusion Module, and the Semantic Attention Conditional Module are integrated into the denoising network to enhance guidance of the diffusion process. Extensive experiments on the BTCV, BraTS‐2020, and ISIC‐2018 datasets demonstrate that RefineCatDiff outperforms several state‐of‐the‐art models in segmentation performance and is highly compatible with various initial‐stage models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/aisy.202401125
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202401125
- OA Status
- gold
- Cited By
- 2
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411704185
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- OpenAlex ID
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https://openalex.org/W4411704185Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/aisy.202401125Digital Object Identifier
- Title
-
RefineCatDiff: Toward High‐Quality Medical Image Segmentation via a Categorical Diffusion Refinement FrameworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-25Full publication date if available
- Authors
-
Feng Liu, Hongjian Yu, Jiaan Huang, Xin Hua, H. J. Liu, Zhanchao Ma, Zhen Wang, Zhijiang Du, Lining SunList of authors in order
- Landing page
-
https://doi.org/10.1002/aisy.202401125Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202401125Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202401125Direct OA link when available
- Concepts
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Categorical variable, Diffusion, Segmentation, Artificial intelligence, Quality (philosophy), Computer science, Image (mathematics), Computer vision, Psychology, Machine learning, Epistemology, Physics, Philosophy, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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22Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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