One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution

• URL: http://arxiv.org/abs/2507.16337v1
• Date: Tue, 22 Jul 2025 08:19:56 GMT
• Title: One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution
• Authors: Xinyu Mao, Xiaohan Xing, Fei Meng, Jianbang Liu, Fan Bai, Qiang Nie, Max Meng,
• Abstract summary: We propose OP-SAM, a One-shot Polyp segmentation framework based on SAM that automatically generates prompts from a single annotated image.<n>Our method introduces Correlation-based Prior Generation (CPG) for semantic label transfer and Scale-cascaded Prior Fusion (SPF) to adapt to polyp size variations.
• Score: 11.042138550601795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Polyp segmentation is vital for early colorectal cancer detection, yet traditional fully supervised methods struggle with morphological variability and domain shifts, requiring frequent retraining. Additionally, reliance on large-scale annotations is a major bottleneck due to the time-consuming and error-prone nature of polyp boundary labeling. Recently, vision foundation models like Segment Anything Model (SAM) have demonstrated strong generalizability and fine-grained boundary detection with sparse prompts, effectively addressing key polyp segmentation challenges. However, SAM's prompt-dependent nature limits automation in medical applications, since manually inputting prompts for each image is labor-intensive and time-consuming. We propose OP-SAM, a One-shot Polyp segmentation framework based on SAM that automatically generates prompts from a single annotated image, ensuring accurate and generalizable segmentation without additional annotation burdens. Our method introduces Correlation-based Prior Generation (CPG) for semantic label transfer and Scale-cascaded Prior Fusion (SPF) to adapt to polyp size variations as well as filter out noisy transfers. Instead of dumping all prompts at once, we devise Euclidean Prompt Evolution (EPE) for iterative prompt refinement, progressively enhancing segmentation quality. Extensive evaluations across five datasets validate OP-SAM's effectiveness. Notably, on Kvasir, it achieves 76.93% IoU, surpassing the state-of-the-art by 11.44%.

Related papers

• Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation [5.951218651336557]
  polyp edges exhibit a remarkably consistent frequency signature across diverse datasets.<n>FPGM learns a domain-invariant frequency prior from the edge regions of labeled polyps.<n>It performs principled spectral perturbations on unlabeled images, aligning their amplitude spectra with this learned prior.<n>It demonstrates exceptional zero-shot generalization capabilities, achieving over 10% absolute gain in Dice score in data-scarce scenarios.
  arXiv  Detail & Related papers  (2025-07-30T16:08:40Z)
• Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation [3.075778955462259]
  Polyp segmentation in colonoscopy images is crucial for early detection and diagnosis of colorectal cancer.<n>We propose SAM-MaGuP, a groundbreaking approach for robust polyp segmentation.<n>By incorporating a boundary distillation module and a 1D-2D Mamba adapter within the Segment Anything Model (SAM), SAM-MaGuP excels at resolving weak boundary challenges.<n>Our key innovations, a Mamba-guided boundary prior and a 1D-2D Mamba block, set a new benchmark in the field, pushing the boundaries of polyp segmentation to new heights.
  arXiv  Detail & Related papers  (2025-07-02T09:16:58Z)
• BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models [6.74659948545092]
  BiSeg-SAM is a weakly supervised prompting and boundary refinement network for the segmentation of polyps and skin lesions.<n>Our method demonstrates significant superiority over state-of-the-art (SOTA) methods when tested on five polyp datasets and one skin cancer dataset.
  arXiv  Detail & Related papers  (2025-04-02T08:04:37Z)
• Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model [7.424888086388194]
  We propose an innovative method named Automatic Polyp with Self-Enriched Semantic Model'' to address limitations in existing approaches. First, we extract a sequence of features from an input image and decode high-level features to generate an initial segmentation mask. Using the proposed self-enriched semantic module, we query potential semantics and augment deep features with additional semantics, thereby aiding the model in understanding context more effectively.
  arXiv  Detail & Related papers  (2024-10-02T03:34:23Z)
• ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
  The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation. SAM's Transformer-based structure prioritizes global and low-frequency information. CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-06-30T14:55:32Z)
• PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation [6.659798925130387]
  We propose a robust fine-tuning technique, PP-SAM, that allows SAM to adapt to the polyp segmentation task with limited images. Experiments on polyp segmentation benchmarks reveal that our variable BBP perturbation significantly improves model resilience. Our results motivate the broader applicability of our PP-SAM for other medical imaging tasks with limited samples.
  arXiv  Detail & Related papers  (2024-05-27T01:13:01Z)
• ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection [88.4359020192429]
  Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training & end-to-end inference framework. Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting
  arXiv  Detail & Related papers  (2024-01-10T07:03:41Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Lesion-aware Dynamic Kernel for Polyp Segmentation [49.63274623103663]
  We propose a lesion-aware dynamic network (LDNet) for polyp segmentation. It is a traditional u-shape encoder-decoder structure incorporated with a dynamic kernel generation and updating scheme. This simple but effective scheme endows our model with powerful segmentation performance and generalization capability.
  arXiv  Detail & Related papers  (2023-01-12T09:53:57Z)
• BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations [79.17754846553866]
  We propose a boosted BoxPolyp model to make full use of both accurate mask and extra coarse box annotations. In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models. Our proposed model outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-07T07:45:50Z)
• Automatic Polyp Segmentation via Multi-scale Subtraction Network [100.94922587360871]
  In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. We propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image.
  arXiv  Detail & Related papers  (2021-08-11T07:54:07Z)
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
  We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. We first aggregate the features in high-level layers using a parallel partial decoder (PPD) In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
  arXiv  Detail & Related papers  (2020-06-13T08:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.