Zero-Shot Gaze-based Volumetric Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.15256v2
• Date: Tue, 10 Jun 2025 10:17:53 GMT
• Title: Zero-Shot Gaze-based Volumetric Medical Image Segmentation
• Authors: Tatyana Shmykova, Leila Khaertdinova, Ilya Pershin,
• Abstract summary: We introduce eye gaze as a novel informational modality for interactive segmentation.<n>We evaluate the performance of using gaze-based prompts with SAM-2 and MedSAM-2 using both synthetic and real gaze data.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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