Superpixel-Guided Segment Anything Model for Liver Tumor Segmentation with Couinaud Segment Prompt

Fei Lyu, Jingwen Xu, Ye Zhu, Grace Lai Hung Wong, Pong C. Yuen*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2024
Subtitle of host publication27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VIII
EditorsMarius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel
PublisherSpringer Cham
Pages678-688
Number of pages11
Edition1st
ISBN (Electronic)9783031721113
ISBN (Print)9783031721106
DOIs
Publication statusPublished - 5 Oct 2024
Event27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco
Duration: 6 Oct 202410 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

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15008
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameMICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention

Conference

Conference27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Country/TerritoryMorocco
CityMarrakesh
Period6/10/2410/10/24
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

User-Defined Keywords

  • Couinaud Segment
  • Liver Tumor Segmentation
  • SAM

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