基于边缘引导框架的 Segment Anything Model 多项分割
摘要
多项分割是医学影像中的关键问题，已提出许多方法旨在提高分割掩码的质量。虽然当前的先进技术产生了令人印象深刻的结果，但这些模型的大小和计算成本为实际行业应用带来了挑战。最近提出的 Segment Anything Model (SAM) 作为一种稳健的基础模型，显示出为医学图像分割提供适应性的前景。 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.
引用
@article{arxiv.2406.14819,
title = {SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation},
author = {Quoc-Huy Trinh and Hai-Dang Nguyen and Bao-Tram Nguyen Ngoc and Debesh Jha and Ulas Bagci and Minh-Triet Tran},
journal= {arXiv preprint arXiv:2406.14819},
year = {2024}
}