Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

TL;DR

SAM-EG tackles boundary-accurate, efficient polyp segmentation by guiding a small Polyp-PVT-based model with semantic features from a frozen Segment Anything Model encoder and an Edge Guidance module that fuses image edges into the learned features. The method adds a simple, edge-aware knowledge transfer objective that aligns SAM embeddings with segmentation embeddings while emphasizing polyp boundaries. Empirical results on multiple datasets show competitive performance with a small parameter budget (~3.7M) and favorable inference costs, including notable improvements on ColonDB and ETIS. This framework offers a practical path toward real-world clinical deployment of polyp segmentation systems by balancing accuracy and efficiency.

Abstract

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.

SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation

TL;DR

SAM-EG tackles boundary-accurate, efficient polyp segmentation by guiding a small Polyp-PVT-based model with semantic features from a frozen Segment Anything Model encoder and an Edge Guidance module that fuses image edges into the learned features. The method adds a simple, edge-aware knowledge transfer objective that aligns SAM embeddings with segmentation embeddings while emphasizing polyp boundaries. Empirical results on multiple datasets show competitive performance with a small parameter budget (~3.7M) and favorable inference costs, including notable improvements on ColonDB and ETIS. This framework offers a practical path toward real-world clinical deployment of polyp segmentation systems by balancing accuracy and efficiency.

Abstract

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.
Paper Structure (19 sections, 3 equations, 2 figures, 4 tables)

This paper contains 19 sections, 3 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Polyp Segmentation
  4. Segment Anything Model (SAM)
  5. Method
  6. SAM Encoder
  7. Segmentation Model
  8. Edge Guiding module (EG)
  9. Objective function
  10. Experiment
  11. Datasets
  12. Implementation Detail
  13. Evaluation Metrics
  14. Performance Comparisons
  15. Result
  16. ...and 4 more sections

Figures (2)

  • Figure 1: General SAM-EG framework
  • Figure 2: Visualization comparison between SAM-EG with several methods