@inproceedings{0ff0df878cbc4bceba6a86e434ab96c4,
title = "Gaze2Segment: A pilot study for integrating eye-tracking technology into medical image segmentation",
abstract = "In this study, we developed a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists{\textquoteright} reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists{\textquoteright} 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.",
keywords = "Eye tracking, Human computer interface, Local saliency, Medical image segmentation, Visual attention",
author = "Naji Khosravan and Haydar Celik and Baris Turkbey and Ruida Cheng and Evan McCreedy and Matthew McAuliffe and Sandra Bednarova and Elizabeth Jones and Xinjian Chen and Peter Choyke and Bradford Wood and Ulas Bagci",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",
year = "2017",
doi = "10.1007/978-3-319-61188-4_9",
language = "English (US)",
isbn = "9783319611877",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "94--104",
editor = "Tal Arbel and Georg Langs and Mark Jenkinson and Bjoern Menze and {Wells III}, {William M.} and Chung, {Albert C.S.} and Kelm, {B. Michael} and Weidong Cai and Albert Montillo and Dimitris Metaxas and Cardoso, {M. Jorge} and Shaoting Zhang and Annemie Ribbens and Henning Muller",
booktitle = "Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers",
}