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


Quoc-Huy Trinh (Aalto University), Hai-Dang Nguyen (Ho Chi Minh city University of Science, Vietnam National University), Nguyen Ngoc Bao Tram (Ho Chi Minh city University of Science, Vietnam National University), Debesh Jha (Northwestern University), Ulas Bagci (Northwestern University), Minh-Triet Tran (Ho Chi Minh city University of Science, Vietnam National University)
The 35th British Machine Vision Conference
PDFPoster

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.

Citation

@inproceedings{Trinh_2024_BMVC,
author    = {Quoc-Huy Trinh and Hai-Dang Nguyen and Nguyen Ngoc Bao Tram and Debesh Jha and Ulas Bagci and Minh-Triet Tran},
title     = {SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0472.pdf}
}


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