Visual Search Patterns in 3D Pancreatic Imaging: An Eye Tracking Study
For researchers in medical imaging and human-computer interaction, this work provides a preliminary taxonomy for analyzing visual search in 3D volumetric data, though it is incremental due to the small sample size and lack of quantitative performance metrics.
This study develops a taxonomy of eye movement data during navigation through 3D CT volumes and applies it to analyze how two radiologists search for the pancreas in abdominal CTs, visualizing gaze behavior in space and time.
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.