  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.
