# Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation

arXiv: https://arxiv.org/abs/1608.03235 · HTML: https://mlsanity.com/paper/1608.03235/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2016.

## Built on by (most cited first, 11 of 11)

- [GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification](https://mlsanity.com/paper/2305.18221/) (2023)
- [Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning](https://mlsanity.com/paper/2205.12466/) (2022)
- [Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning](https://mlsanity.com/paper/2403.12416/) (2024)
- [Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation](https://mlsanity.com/paper/2101.09014/) (2021)
- [Gaze-Assisted Medical Image Segmentation](https://mlsanity.com/paper/2410.17920/) (2024)
- [On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2202.02764/) (2022)
- [A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision](https://mlsanity.com/paper/2107.08394/) (2021)
- [Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis](https://mlsanity.com/paper/2203.02794/) (2022)
- [GazeXPErT: An Expert Eye-tracking Dataset for Interpretable and Explainable AI in Oncologic FDG-PET/CT Scans](https://mlsanity.com/paper/2603.00162/) (2026)
- [Measuring Browser Webcam Gaze Honestly: A Capture-Clock Methodology and Open Reference Implementation](https://mlsanity.com/paper/2608.11566/) (2026)
- [GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation](https://mlsanity.com/paper/2609.01310/) (2026)

## Abstract (arXiv)

This study introduced a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists' reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map. This map was then combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a object of interest. With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest. At the final step, these cues were used to initiate a seed-based delineation process. Segmentation accuracy of the proposed Gaze2Segment was found to be 86% with dice similarity coefficient and 1.45 mm with Hausdorff distance. To the best of our knowledge, Gaze2Segment is the first true integration of eye-tracking technology into a medical image segmentation task without the need for any further user-interaction.