Gaze2Segment: A Pilot Study For Integrating Eye-Tracking Technology Into Medical Image Segmentation

Keywords

Eye tracking; Human computer interface; Local saliency; Medical image segmentation; Visual attention

Abstract

In this study, we developed 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. Next, this map was 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 region of interest.With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest found in the previous step. These cues are used to initiate a seed-based delineation process. The proposed Gaze2Segment achieved a dice similarity coefficient of 86% and Hausdorff distance of 1.45 mm as a segmentation accuracy. 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.

Publication Date

1-1-2017

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10081 LNCS

Number of Pages

94-104

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-61188-4_9

Socpus ID

85025165709 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85025165709

This document is currently not available here.

Share

COinS