When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

• URL: http://arxiv.org/abs/2304.08506v6
• Date: Thu, 21 Dec 2023 07:49:49 GMT
• Title: When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation
• Authors: Chuanfei Hu, Tianyi Xia, Shenghong Ju, Xinde Li
• Abstract summary: Segment Anything Model (SAM) performs the significant zero-shot image segmentation. We investigate the capability of SAM for medical image analysis, especially for multi-phase liver tumor segmentation.
• Score: 4.154974672747996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analysis, especially for multi-phase liver tumor segmentation (MPLiTS), in terms of prompts, data resolution, phases. Experimental results demonstrate that there might be a large gap between SAM and expected performance. Fortunately, the qualitative results show that SAM is a powerful annotation tool for the community of interactive medical image segmentation.

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