发表机构
Université Sorbonne Paris Nord; Northwestern University; VSB-Technical University of Ostrava（巴黎北索邦大学; 西北大学; 俄斯特拉发VSB技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GazeRefine是一种无训练框架，以注视为推理提示，在DINOv3特征空间中生成并细化语义原型，在息肉和前列腺MRI分割任务上取得良好性能，为标签高效的人在回路医学图像分割提供新方案。
AI 中文摘要
医学图像分割的规模化应用仍面临困难，因为高性能方法通常依赖密集的专家标注和特定任务的训练。我们提出GazeRefine，这是一种无训练框架，将注视作为推理时的提示用于零样本医学图像分割。稀疏的、按持续时间加权的注视点被转换为前景和背景先验，在冻结的DINOv3特征空间中初始化语义原型。这些原型通过前景-背景判别、特征空间亲和传播以及锚定到初始注视引导进行迭代细化，使得分割可以扩展到直接注视区域之外，同时限制语义漂移。GazeRefine不需要分割掩码、微调、适配器、提示编码器或梯度更新。我们在带有注视标注的息肉分割和前列腺MRI分割上评估该方法，结果显示其在结肠镜检查图像上表现强劲，在前列腺MRI上表现具有竞争力，支持注视引导的原型细化作为一种有前景的方法，用于标签效率高、人在回路中的医学图像分割。我们的工具和代码可在以下代码库获取：this https URL
英文摘要
Medical image segmentation remains difficult to scale because high-performing methods typically rely on dense expert annotations and task-specific training. We introduce GazeRefine, a training-free framework that uses gaze as an inference-time prompt for zero-shot medical image segmentation. Sparse, duration-weighted fixations are converted into foreground and background priors that initialize semantic prototypes in frozen DINOv3 feature space. These prototypes are iteratively refined through foreground-background discrimination, feature-space affinity propagation, and anchoring to the initial gaze guidance, allowing segmentation to extend beyond directly fixated regions while limiting semantic drift. GazeRefine requires no segmentation masks, fine-tuning, adapters, prompt encoders, or gradient updates. We evaluate the method on gaze-annotated polyp segmentation and prostate MRI segmentation. The results show strong performance on colonoscopy images and competitive performance on prostate MRI, supporting gaze-guided prototype refinement as a promising approach for segmentation-label-efficient, human-in-the-loop medical image segmentation. Our tools and code can be found in the following repository: https://github.com/MohammedOussamaBEN/GazeRefine.git
Comments9 pages, 5 figures. Accepted at MICCAI Workshop 2026
