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.

在医学成像中，息肉分割是一个关键问题，引发了许多旨在提高分割掩模质量的方法。尽管当前最先进的技术取得了令人印象深刻的结果，但这些模型的规模和计算成本对实际的行业应用构成了挑战。最近，提出了 Segment Anything Model (SAM) 作为一种稳健的基础模型，显示出对于医学图像分割的适应性的前景。受到这一概念的启发，我们提出了 SAM-EG，这是一个指导小型息肉分割模型来解决计算成本挑战的框架。此外，在这项研究中，我们引入了 Edge Guiding 模块，将边缘信息集成到图像特征中，以帮助分割模型解决当前分割模型在此任务中的边界问题。通过大量实验，我们的小型模型展示了它们的功效，通过达到与最先进的方法相竞争的结果，为息肉分割和医学成像领域的紧凑模型开发提供了一种有希望的方法。