Zero-Shot Gaze-based Volumetric Medical Image Segmentation
This work addresses the need for more efficient interaction methods in clinical applications like disease monitoring and cancer treatment planning, though it is incremental as it builds on existing models like SAM-2 and MedSAM-2.
The study tackled the problem of interactive segmentation in volumetric medical images by introducing eye gaze as a novel prompt modality, finding that it offers a time-efficient approach with slightly lower segmentation quality compared to bounding boxes.
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.