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Visual Search Patterns in 3D Pancreatic Imaging: An Eye Tracking Study
Preprint   Open access

Visual Search Patterns in 3D Pancreatic Imaging: An Eye Tracking Study

Anna Anikina, Leila Khaertdinova, Trine Balschmidt, Michael B Andersen, Christoph F Müller, Erik GS Brandt, Henrik S Thomsen, Claudia Mello-Thoms and Bulat Ibragimov
ArXiv.org
Cornell University
05/13/2026
DOI: 10.48550/arxiv.2605.16408
url
https://doi.org/10.48550/arxiv.2605.16408View
Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Proc. SPIE 13928, Medical Imaging 2026: Image Perception, Observer Performance, and Technology Assessment, 1392814 (2026) Eye tracking has emerged as a powerful tool for examining visual perception and search strategies in various domains, including medicine. While it is relatively straightforward to apply in 2D settings, its use in 3D medical imaging remains challenging and not yet well explored. This gap is particularly relevant for radiology, where volumetric images such as computed tomography (CT) scans are routinely read by medical experts. Radiologists typically interpret these images by navigating through hundreds of 2D slices, most often viewed in the axial projection. A taxonomy of eye movement data during navigation through a CT volume could be valuable to understand how radiologists approach diagnostic tasks. As an example of the derived taxonomy, we asked two radiologists to search abdominal CTs of the pancreas. We collect eye tracking data and align eye gaze movements with slice navigation to visualize the representation of the pancreas through volume and analyze clinicians' gaze behavior in both space and time.
 Computer Science - Computer Vision and Pattern Recognition

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