SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation
This work addresses the need for efficient polyp segmentation models in medical imaging, though it is incremental as it builds on existing methods like SAM.
The paper tackles the computational cost challenge in polyp segmentation by proposing SAM-EG, a framework that guides small models using edge information, achieving competitive results with state-of-the-art methods.
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.