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# Polyp Segmentation

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arXiv Sep 25, 2026
RPG-SAM pairs reliability-weighted prototypes with geometric adaptive thresholds to lift training-free one-shot polyp segmentation by 5.56% mIoU on Kvasir
The work proposes RPG-SAM, a SAM2-based training-free one-shot polyp segmentation framework that uses Reliability-Weighted Prototype Mining (RWPM) to weight support foreground prototypes by a contrast factor and a reverse purity factor while using background prototypes as negative anchors for noise suppression, Geometric Adaptive Selection (GAS) to pick binarization thresholds dynamically from morphological solidity and scale consensus, and a Prior-guided Iterative Refinement (PIR) loop to polish boundaries, reaching 78.65% mIoU and 85.65% mDice on Kvasir, surpassing ProtoSAM by 5.56% and 4.11% respectively, with comparisons also reported on three-center PolypGen, CVC-ClinicDB, and CVC-ColonDB.
The work proposes RPG-SAM, a SAM2-based training-free one-shot polyp segmentation framework that uses Reliability-Weighted Prototype Mining (RWPM) to weight support foreground prototypes by a contrast factor and a reverse purity factor while using background prototypes as negative anchors for noise suppression, Geometric Adaptive Selection (GAS) to pick binarization thresholds dynamically from morphological solidity and scale consensus, and a Prior-guided Iterative Refinement (PIR) loop to polish boundaries, reaching 78.65% mIoU and 85.65% mDice on Kvasir, surpassing ProtoSAM by 5.56% and 4.11% respectively, with comparisons also reported on three-center PolypGen, CVC-ClinicDB, and CVC-ColonDB.
East China Normal University
Weikun Lin（First author）Yan Wang（Corresponding author）
RPG-SAM Polyp Segmentation One-Shot Segmentation Reliability-Weighted Prototype Mining Segment Anything Model
Journal of Imaging Informatics in Medicine Sep 15, 2026
FAR-POLYP-SEG: A Prospective Single-Center Colonoscopy Dataset for Colorectal Polyp Segmentation with Patient-Level Metadata and Baseline Cross-Dataset Evaluation
This work prospectively collected 8,181 frames from 455 patients during routine colonoscopy at Farhikhtegan Hospital, Tehran, Iran, between February and December 2025 (432 polyp-positive frames with expert pixel-level segmentation masks and 7,749 normal-mucosa frames), linked patient-level metadata (age, sex, colonoscopy indication, BBPS score, and procedure duration) to every case, and trained and evaluated six segmentation architectures under one standardized protocol with patient-grouped five-fold cross-validation, finding that PraNet reached the highest internal Dice (0.755) and nnU-Net the highest internal IoU (0.665) and pixel accuracy, yet gated false-positive rates on normal mucosa ranged from 24.9% (YOLOv11m-seg) to 59.
This work prospectively collected 8,181 frames from 455 patients during routine colonoscopy at Farhikhtegan Hospital, Tehran, Iran, between February and December 2025 (432 polyp-positive frames with expert pixel-level segmentation masks and 7,749 normal-mucosa frames), linked patient-level metadata (age, sex, colonoscopy indication, BBPS score, and procedure duration) to every case, and trained and evaluated six segmentation architectures under one standardized protocol with patient-grouped five-fold cross-validation, finding that PraNet reached the highest internal Dice (0.755) and nnU-Net the highest internal IoU (0.665) and pixel accuracy, yet gated false-positive rates on normal mucosa ranged from 24.9% (YOLOv11m-seg) to 59.
Tehran University of Medical Sciences Islamic Azad University, Tehran University of Tabriz Alborz University of Medical Sciences University of Southern Denmark Amirkabir University of Technology
Mehrshad Lalinia（First author）Ali Sahafi（Corresponding author）
FAR-POLYP-SEG Colorectal Polyp Segmentation Polyp Segmentation

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