Papers › SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation
SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation
Quoc-Huy Trinh, Hai-Dang Nguyen, Bao-Tram Nguyen Ngoc, Debesh Jha, Ulas Bagci, Minh-Triet Tran
Polyp segmentation, a critical concern in medical imaging, has prompted numerous proposed methods aimed at enhancing the quality of segmented masks. While current state-of-the-art techniques produce impressive results, the size and computational cost of these models pose challenges for practical industry applications. Recently, the Segment Anything Model (SAM) has been proposed as a robust foundation model, showing promise for adaptation to medical image segmentation. Inspired by this concept, we propose SAM-EG, a framework that guides small segmentation models for polyp segmentation to address the computation cost challenge. Additionally, in this study, we introduce the Edge Guiding module, which integrates edge information into image features to assist the segmentation model in addressing boundary issues from current segmentation model in this task. Through extensive experiments, our small models showcase their efficacy by achieving competitive results with state-of-the-art methods, offering a promising approach to developing compact models with high accuracy for polyp segmentation and in the broader field of medical imaging.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Image Segmentation | CVC-ClinicDB | SAM-EG | mIoU | 0.879 | #30 of 48 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ClinicDB | SAM-EG | mean Dice | 0.931 | #30 of 48 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ColonDB | SAM-EG | mIoU | 0.689 | #16 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ColonDB | SAM-EG | mean Dice | 0.774 | #16 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | ETIS-LARIBPOLYPDB | SAM-EG | mIoU | 0.681 | #14 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | ETIS-LARIBPOLYPDB | SAM-EG | mean Dice | 0.757 | #14 of 25 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | SAM-EG | mIoU | 0.862 | #29 of 58 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | SAM-EG | mean Dice | 0.915 | #29 of 58 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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