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Can SAM Segment Polyps?

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arxiv 2304.07583 v1 pith:LMXLUEZJ submitted 2023-04-15 cs.CV

Tao Zhou , Yizhe Zhang , Yi Zhou , Ye Wu , Chen Gong This is my paper · ORCID
classification cs.CV
keywords segmentationpolypfieldinterestingperformancepolypsreportsegment
verification ladder T0 review T1 audit T2 compute T3 formal

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Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SAFIRE: Segment Any Forged Image Region

    cs.CV 2024-12 conditional novelty 7.0 of 10

    SAFIRE uses point prompting and feature clustering to partition forged images into multiple source regions, and reports state-of-the-art results on both binary forgery localization and a new multi-source partitioning task.

  2. Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SAM-MaGuP, a SAM-based polyp segmentation model with a 1D-2D Mamba adapter and boundary distillation, reports state-of-the-art mDice/mIoU on five public colonoscopy datasets.

  3. Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A unified evaluation of SAM and SAM 2 on 11 context-dependent concepts over 33 datasets shows box prompts dominate, SAM 2 lags SAM in some static-image settings, and both are prompt-sensitive.

  4. Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Sli2Vol+ learns slice-to-slice correspondences with guidance from pseudo-labels, improving single-slice-annotated 3D segmentation over the Sli2Vol baseline by about 5.6 Dice points on CT and 3.5 on MRI.

  5. SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation

    cs.CV 2024-12 conditional novelty 5.0 of 10

    SAM-Mamba couples a Mamba-based prior with a frozen SAM encoder and adapter fine-tuning to achieve state-of-the-art polyp segmentation and cross-dataset zero-shot generalization.

  6. Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

    cs.CV 2025-02 conditional novelty 3.0 of 10

    Self-Prompt-SAM automatically generates point, box, and mask prompts for a fine-tuned SAM and reports state-of-the-art Dice scores on three medical segmentation benchmarks.

  7. Recent Advances in Medical Imaging Segmentation: A Survey

    cs.CV 2025-05 unverdicted novelty 1.0 of 10

    A survey of four recent deep learning paradigms for medical image segmentation, summarizing methods, datasets, results, and open problems without contributing new experimental results.

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