Abstract
The Segment Anything Model (SAM) is a powerful foundation model which has shown impressive performance for generic image segmentation. However, directly applying SAM to liver tumor segmentation presents challenges due to the domain gap between nature images and medical images, and the requirement of labor-intensive manual prompt generation. To address these challenges, we first investigate text promptable liver tumor segmentation by Couinaud segment, where Couinaud segment prompt can be automatically extracted from radiology reports to reduce massive manual efforts. Moreover, we propose a novel CouinaudSAM to adapt SAM for liver tumor segmentation. Specifically, we achieve this by: 1) a superpixel-guided prompt generation approach to effectively transform Couinaud segment prompt into SAM-acceptable point prompt; and 2) a difficulty-aware prompt sampling strategy to make model training more effective and efficient. Experimental results on the public liver tumor segmentation dataset demonstrate that our method outperforms the other state-of-the-art methods.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 |
Subtitle of host publication | 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VIII |
Editors | Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel |
Publisher | Springer Cham |
Pages | 678-688 |
Number of pages | 11 |
Edition | 1st |
ISBN (Electronic) | 9783031721113 |
ISBN (Print) | 9783031721106 |
DOIs | |
Publication status | Published - 5 Oct 2024 |
Event | 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco Duration: 6 Oct 2024 → 10 Oct 2024 https://link.springer.com/book/10.1007/978-3-031-72083-3 (Conference Proceedings (Part IV)) https://link.springer.com/book/10.1007/978-3-031-72111-3 (Conference Proceedings (Part VIII)) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 15008 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Name | MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention |
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Conference
Conference | 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 |
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Country/Territory | Morocco |
City | Marrakesh |
Period | 6/10/24 → 10/10/24 |
Internet address |
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Scopus Subject Areas
- Theoretical Computer Science
- General Computer Science
User-Defined Keywords
- Couinaud Segment
- Liver Tumor Segmentation
- SAM