A mutual inclusion mechanism for precise boundary segmentation in medical images
The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.
Authors
- Teeradaj Racharak (ORCID: https://orcid.org/0000-0002-8823-2361)
- Le-Minh Nguyen (ORCID: https://orcid.org/0000-0002-2265-1010)
- Tianhua Yang (ORCID: https://orcid.org/0000-0001-5769-1146)
- Kai Li (ORCID: https://orcid.org/0009-0005-9403-3085)
- Siqi Li (ORCID: https://orcid.org/0000-0001-9720-826X)
- Guanqun Sun (ORCID: https://orcid.org/0009-0008-4704-7072)
- Yizhi Pan
- Junyi Xin (ORCID: https://orcid.org/0000-0003-2414-3260)
Institutions
- Japan Advanced Institute of Science and Technology (JP)
- Zhejiang Provincial People's Hospital (CN)
- Hangzhou Medical College (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2024-12-24
- DOI
- https://doi.org/10.3389/fbioe.2024.1504249
- Citations
- 20
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.09
Funders
- National Natural Science Foundation of China