🤖 AI Summary
This study addresses the lack of systematic analysis of radiologists’ visual search patterns in 3D medical imaging, particularly the poorly understood eye movement behavior during pancreatic CT interpretation. The work proposes a novel analytical framework that integrates slice navigation with eye-tracking data, enabling spatiotemporal alignment of gaze behavior as radiologists examine volumetric abdominal CT scans of the pancreas. By synchronously recording and analyzing these multimodal signals, the study introduces a classification method tailored to visual search behaviors in 3D medical images. The approach successfully visualizes the gaze trajectories and search strategies of two expert radiologists within the pancreatic region, offering both empirical insights and methodological innovation for understanding the visual cognitive mechanisms underlying diagnostic decision-making in high-dimensional medical imaging.
📝 Abstract
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.