# Zero-Shot Gaze-based Volumetric Medical Image Segmentation

arXiv: https://arxiv.org/abs/2505.15256 · HTML: https://mlsanity.com/paper/2505.15256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 5 of 5)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation](https://mlsanity.com/paper/2409.19483/) (2024)
- [GazeSAM: What You See is What You Segment](https://mlsanity.com/paper/2304.13844/) (2023)
- [Gaze-Assisted Medical Image Segmentation](https://mlsanity.com/paper/2410.17920/) (2024)

## Built on by (most cited first, 2 of 2)

- [FixationFormer: Direct Utilization of Expert Gaze Trajectories for Chest X-Ray Classification](https://mlsanity.com/paper/2603.22939/) (2026)
- [GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation](https://mlsanity.com/paper/2609.01310/) (2026)

## Abstract (arXiv)

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.