Zero-Shot Gaze-based Volumetric Medical Image Segmentation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915334991118336 |
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| author | Shmykova, Tatyana Khaertdinova, Leila Pershin, Ilya |
| author_facet | Shmykova, Tatyana Khaertdinova, Leila Pershin, Ilya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Gaze-based Volumetric Medical Image Segmentation Shmykova, Tatyana Khaertdinova, Leila Pershin, Ilya Computer Vision and Pattern Recognition Artificial Intelligence I.2.1 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. |
| title | Zero-Shot Gaze-based Volumetric Medical Image Segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.1 |
| url | https://arxiv.org/abs/2505.15256 |