用YOLO+SAM2自动分割肠镜中的息肉，省去人工标注。
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
- 用YOLOv8预测框自动生成SAM2输入提示，实现端到端分割。
- 在5个图像和2个视频数据集上均超越现有模型，精度显著提升。
- 仅需框标注即可高精度分割，适合临床快速部署。
结直肠癌（CRC）的早期诊断与治疗依赖于肠镜中息肉的及时发现。然而，息肉形态差异大且肠镜图像与视频常含伪影，给精准高效检测与分割带来挑战。本文提出一种新方法，将分割任意模型（SAM 2）与YOLOv8结合，利用YOLOv8的边界框预测自动生成SAM 2的输入提示，大幅减少人工标注需求。我们在五个基准肠镜图像数据集和两个肠镜视频数据集上进行了全面测试，结果表明该方法在图像与视频分割任务中均优于当前最优模型。尤其在仅使用边界框标注的情况下，仍可实现高精度分割，显著降低标注时间与成本。该技术有望提升临床环境下息肉检测的效率与可扩展性。
原文摘要 · Abstract (English)
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.
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