Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation

  • Naji Khosravan
  • Haydar Celik
  • Baris Turkbey
  • Ruida Cheng
  • Evan McCreedy
  • Matthew McAuliffe
  • Sandra Bednarova
  • Elizabeth Jones
  • Xinjian Chen
  • Peter Choyke
  • Bradford Wood
  • Ulas Bagci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10081)

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.

Keywords

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Naji Khosravan
    • 1
  • Haydar Celik
    • 2
  • Baris Turkbey
    • 2
  • Ruida Cheng
    • 2
  • Evan McCreedy
    • 2
  • Matthew McAuliffe
    • 2
  • Sandra Bednarova
    • 2
  • Elizabeth Jones
    • 2
  • Xinjian Chen
    • 3
  • Peter Choyke
    • 2
  • Bradford Wood
    • 2
  • Ulas Bagci
    • 1
  1. 1.Center for Research in Computer Vision (CRCV)University of Central Florida (UCF)OrlandoUSA
  2. 2.National Institutes of Health (NIH)BethesdaUSA
  3. 3.Soochow UniversitySuzhou CityChina

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