Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions
Abstract Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis. Lesion areas usually have c...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87829-4 |
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| author | Yuan Tian Yongquan Liang Yufeng Chen Jingjing Zhang Hongyang Bian |
| author_facet | Yuan Tian Yongquan Liang Yufeng Chen Jingjing Zhang Hongyang Bian |
| author_sort | Yuan Tian |
| collection | DOAJ |
| description | Abstract Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis. Lesion areas usually have complex shapes and blurred edges. Lesion segmentation requires more attention to deal with the diversity and uncertainty of lesions. To address this challenge, we propose MSPO-Net, a multilevel support-assisted prototype optimization network designed for few-shot lesion segmentation in computerized tomography (CT) images of lung diseases. MSPO-Net learns lesion prototypes from low-level to high-level features. Self-attention threshold learning strategy can focus on the global information and obtain an optimal threshold for CT images. Our model refines prototypes through a support-assisted prototype optimization module, adaptively enhancing their representativeness for the diversity of lesions and adapting to the unseen lesions better. In clinical examinations, CT is more practical than X-rays. To ensure the quality of our work, we have established a small-scale CT image dataset for three lung diseases and annotated by experienced doctors. Experiments demonstrate that MSPO-Net can improve segmentation performance and robustness of lung disease lesion. MSPO-Net achieves state-of-the-art performance in both single and unseen lung disease segmentation, indicating its potentiality to reduce doctors’ workload and improve diagnostic accuracy. This research has certain clinical significance. Code is available at https://github.com/Tian-Yuan-ty/MSPO-Net . |
| format | Article |
| id | doaj-art-2a82aa5039dc472ca7942426b6ed9fc7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-2a82aa5039dc472ca7942426b6ed9fc72025-01-26T12:31:48ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-87829-4Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesionsYuan Tian0Yongquan Liang1Yufeng Chen2Jingjing Zhang3Hongyang Bian4College of Computer Science and Engineering, Shandong University of Science and TechnologyCollege of Computer Science and Engineering, Shandong University of Science and TechnologyShandong Provincial Public Health Clinical Center, Shandong UniversityShandong Provincial Public Health Clinical Center, Shandong UniversityShandong Provincial Public Health Clinical Center, Shandong UniversityAbstract Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis. Lesion areas usually have complex shapes and blurred edges. Lesion segmentation requires more attention to deal with the diversity and uncertainty of lesions. To address this challenge, we propose MSPO-Net, a multilevel support-assisted prototype optimization network designed for few-shot lesion segmentation in computerized tomography (CT) images of lung diseases. MSPO-Net learns lesion prototypes from low-level to high-level features. Self-attention threshold learning strategy can focus on the global information and obtain an optimal threshold for CT images. Our model refines prototypes through a support-assisted prototype optimization module, adaptively enhancing their representativeness for the diversity of lesions and adapting to the unseen lesions better. In clinical examinations, CT is more practical than X-rays. To ensure the quality of our work, we have established a small-scale CT image dataset for three lung diseases and annotated by experienced doctors. Experiments demonstrate that MSPO-Net can improve segmentation performance and robustness of lung disease lesion. MSPO-Net achieves state-of-the-art performance in both single and unseen lung disease segmentation, indicating its potentiality to reduce doctors’ workload and improve diagnostic accuracy. This research has certain clinical significance. Code is available at https://github.com/Tian-Yuan-ty/MSPO-Net .https://doi.org/10.1038/s41598-025-87829-4Few-shot lesion segmentationSelf-attention threshold learning strategySupport-assisted prototype optimizationLung diseasesChest CT image |
| spellingShingle | Yuan Tian Yongquan Liang Yufeng Chen Jingjing Zhang Hongyang Bian Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions Scientific Reports Few-shot lesion segmentation Self-attention threshold learning strategy Support-assisted prototype optimization Lung diseases Chest CT image |
| title | Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions |
| title_full | Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions |
| title_fullStr | Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions |
| title_full_unstemmed | Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions |
| title_short | Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions |
| title_sort | multilevel support assisted prototype optimization network for few shot medical segmentation of lung lesions |
| topic | Few-shot lesion segmentation Self-attention threshold learning strategy Support-assisted prototype optimization Lung diseases Chest CT image |
| url | https://doi.org/10.1038/s41598-025-87829-4 |
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