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

Quoc-Huy Trinh, Hai-Dang Nguyen, Nguyen Ngoc Bao Tram, Debesh Jha, Ulas Bagci, Minh-Triet Tran

Abstract: Polyp segmentation, a critical challenge in medical imaging, has prompted numerous proposed methods to enhance the quality of segmented masks. While current state-of-the-art techniques produce impressive results, these models' size and computational cost 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 the 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.