基于零样本注视的体积医学图像分割
Zero-Shot Gaze-based Volumetric Medical Image Segmentation
AI总结：
针对三维医学图像交互式分割依赖手动提示的问题，本文提出将眼动注视作为零样本提示模态，并在 SAM-2 与 MedSAM-2 上用合成及真实数据验证其省时且可作为互补输入的价值。
摘要：
在体积医学图像中准确分割解剖结构，对于疾病监测和癌症治疗规划等临床应用至关重要。当代交互式分割模型，例如 Segment Anything Model 2（SAM-2）及其医学变体 MedSAM-2，依赖边界框和鼠标点击等手动提供的提示。本研究引入眼动注视作为交互式分割的一种新型信息模态，标志着眼动追踪在三维医学图像分割中的应用。我们使用合成和真实注视数据，评估了 SAM-2 与 MedSAM-2 采用基于注视的提示时的性能。与边界框相比，基于注视的提示提供了一种更省时的交互方式，而分割质量略低。我们的研究结果凸显了将注视作为交互式三维医学图像分割的互补输入模态的潜力。
英文摘要：
Accurate segmentation of anatomical structures in volumetric medical images is crucial for clinical applications, including disease monitoring and cancer treatment planning. Contemporary interactive segmentation models, such as Segment Anything Model 2 (SAM-2) and its medical variant (MedSAM-2), rely on manually provided prompts like bounding boxes and mouse clicks. In this study, we introduce eye gaze as a novel informational modality for interactive segmentation, marking the application of eye-tracking for 3D medical image segmentation. We evaluate the performance of using gaze-based prompts with SAM-2 and MedSAM-2 using both synthetic and real gaze data. Compared to bounding boxes, gaze-based prompts offer a time-efficient interaction approach with slightly lower segmentation quality. Our findings highlight the potential of using gaze as a complementary input modality for interactive 3D medical image segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
