Refining CT image analysis: Exploring adaptive fusion in U-nets for enhanced brain tissue segmentation
Our adaptive fusion strategy significantly diminishes FAs and enhances the training efficacy of DL-based ILS algorithms, surpassing individual U-Net models. This methodology holds promise as a versatile, data-independent approach for cerebral lesion segmentation.
Authors
- Ren‐Hung Hwang (ORCID: https://orcid.org/0000-0001-7996-4184)
- Wei‐Min Liu (ORCID: https://orcid.org/0000-0002-3617-1739)
- Wen-Chi Chiang
- Bang-Chuan Chen (ORCID: https://orcid.org/0000-0003-1461-1833)
- Jyh‐Wen Chai
- Chung-Yi Shen
- Chi-Hsiang Chou
Institutions
- National Chung Cheng University (TW)
- National Yang Ming Chiao Tung University (TW)
- National Chung Hsing University (TW)
- Taichung Veterans General Hospital (TW)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1371/journal.pone.0323692
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Taichung Veterans General Hospital