bibtext: @InProceedings{ Lyu_SuperpixelGuided_MICCAI2024,
                   author = { Lyu, Fei and Xu, Jingwen and Zhu, Ye and Wong, Grace Lai-Hung and Yuen, Pong C. },
                   title = { { Superpixel-Guided Segment Anything Model for Liver Tumor Segmentation with Couinaud Segment Prompt } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Superpixel-Guided Segment Anything Model for Liver Tumor Segmentation with Couinaud Segment Prompt
authors: Lyu, Fei Xu, Jingwen Zhu, Ye Wong, Grace Lai-Hung Yuen, Pong C.