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MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM
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The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to varying prompt types and locations poses challenges. This paper addresses these challenges by focusing on the development of reliable prompts that enhance MedSAM's accuracy. We introduce MedSAM-U, an uncertainty-guided framework designed to automatically refine multi-prompt inputs for more reliable and precise medical image segmentation. Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, creating MPA-MedSAM, to adapt to diverse multi-prompt inputs. We then employ uncertainty-guided multi-prompt to effectively estimate the uncertainties associated with the prompts and their initial segmentation results. In particular, a novel uncertainty-guided prompts adaptation technique is then applied automatically to derive reliable prompts and their corresponding segmentation outcomes. We validate MedSAM-U using datasets from multiple modalities to train a universal image segmentation model. Compared to MedSAM, experimental results on five distinct modal datasets demonstrate that the proposed MedSAM-U achieves an average performance improvement of 1.7\% to 20.5\% across uncertainty-guided prompts.
Forward citations
Cited by 6 Pith papers
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Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting
DFG automatically finds bounding-box prompts for MedSAM and uses the refined masks as pseudo-labels, improving source-free cross-domain medical image segmentation.
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E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation
The submission pairs an abstract for an efficient Bayesian SAM variant (E-BayesSAM) with a full text on Lambert W function convexity by a different author, leaving the main claims unsupported by any visible methods.
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Compress Any Segment Anything Model (SAM)
Birkhoff compresses 18 SAM variants to about one-fifth their size with less than 1% accuracy loss, data-free, using a trajectory-based codebook and a fused GPU operator.
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Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
A broad survey that organizes prompt mechanisms for medical image generation, segmentation, and classification into a two-dimensional taxonomy of core technologies and clinical applications.
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Diffusion-empowered AutoPrompt MedSAM
A class-index-driven diffusion-style prompt encoder turns MedSAM into a fully automatic segmenter that outputs semantically labeled masks, with reported gains on CT, MRI, endoscopy, and X-ray benchmarks.
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Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
A structured survey of prompt engineering methods for the Segment Anything Model, covering geometric, textual, and multimodal prompts and their applications.
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