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Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
March 19, 2024, 4:51 a.m. | Yichi Zhang, Shiyao Hu, Sijie Ren, Chen Jiang, Yuan Cheng, Yuan Qi
cs.CV updates on arXiv.org arxiv.org
Abstract: The Segment Anything Model (SAM) has recently emerged as a groundbreaking foundation model for prompt-driven image segmentation tasks. However, both the original SAM and its medical variants require slice-by-slice manual prompting of target structures, which directly increase the burden for applications. Despite attempts of auto-prompting to turn SAM into a fully automatic manner, it still exhibits subpar performance and lacks of reliability especially in the field of medical imaging. In this paper, we propose UR-SAM, …
abstract arxiv auto cs.cv foundation foundation model groundbreaking however image medical prompt prompting reliability sam segment segment anything segment anything model segmentation slice tasks type uncertainty variants
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