Early diagnosis and treatment of polyps during colonoscopy are essential for reducing the incidence and mortality of Colorectal Cancer (CRC). However, the variability in polyp characteristics and the presence of artifacts in colonoscopy images and videos pose significant challenges for accurate and efficient polyp detection and segmentation. This paper presents a novel approach to polyp segmentation by integrating the Segment Anything Model (SAM 2) with the YOLOv8 model. Our method leverages YOLOv8's bounding box predictions to autonomously generate input prompts for SAM 2, thereby reducing the need for manual annotations. We conducted exhaustive tests on five benchmark colonoscopy image datasets and two colonoscopy video datasets, demonstrating that our method exceeds state-of-the-art models in both image and video segmentation tasks. Notably, our approach achieves high segmentation accuracy using only bounding box annotations, significantly reducing annotation time and effort. This advancement holds promise for enhancing the efficiency and scalability of polyp detection in clinical settings https://github.com/sajjad-sh33/YOLO_SAM2.

本研究针对结肠镜检查中腺瘤检测和分割的准确性和效率问题，通过将Segment Anything Model (SAM 2)与YOLOv8模型相结合，提出了一种新颖的腺瘤分割方法。该方法利用YOLOv8的边界框预测自主生成输入提示，显著提升分割精度，并减少人工标注的需求，为临床腺瘤检测的效率和可扩展性提供了有力支持。

基于混合YOLO-SAM 2模型的腺瘤分割自提示方法