Literature DB >> 30188843

Evaluation of Gaze Tracking Calibration for Longitudinal Biomedical Imaging Studies.

Pierre Chatelain, Harshita Sharma, Lior Drukker, Aris T Papageorghiou, J Alison Noble.   

Abstract

Gaze tracking is a promising technology for studying the visual perception of clinicians during image-based medical exams. It could be used in longitudinal studies to analyze their perceptive process, explore human-machine interactions, and develop innovative computer-aided imaging systems. However, using a remote eye tracker in an unconstrained environment and over time periods of weeks requires a certain guarantee of performance to ensure that collected gaze data are fit for purpose. We report the results of evaluating eye tracking calibration for longitudinal studies. First, we tested the performance of an eye tracker on a cohort of 13 users over a period of one month. For each participant, the eye tracker was calibrated during the first session. The participants were asked to sit in front of a monitor equipped with the eye tracker, but their position was not constrained. Second, we tested the performance of the eye tracker on sonographers positioned in front of a cart-based ultrasound scanner. Experimental results show a decrease of accuracy between calibration and later testing of 0.30° and a further degradation over time at a rate of 0.13°. month-1. The overall median accuracy was 1.00° (50.9 pixels) and the overall median precision was 0.16° (8.3 pixels). The results from the ultrasonography setting show a decrease of accuracy of 0.16° between calibration and later testing. This slow degradation of gaze tracking accuracy could impact the data quality in long-term studies. Therefore, the results we present here can help in planning such long-term gaze tracking studies.

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Year:  2018        PMID: 30188843     DOI: 10.1109/TCYB.2018.2866274

Source DB:  PubMed          Journal:  IEEE Trans Cybern        ISSN: 2168-2267            Impact factor:   11.448


  5 in total

1.  Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention.

Authors:  Richard Droste; Yifan Cai; Harshita Sharma; Pierre Chatelain; Lior Drukker; Aris T Papageorghiou; J Alison Noble
Journal:  Inf Process Med Imaging       Date:  2019-05-22

2.  Multi-Modal Learning from Video, Eye Tracking, and Pupillometry for Operator Skill Characterization in Clinical Fetal Ultrasound.

Authors:  Harshita Sharma; Lior Drukker; Aris T Papageorghiou; J Alison Noble
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2021-05-25

3.  Eye tracking: empirical foundations for a minimal reporting guideline.

Authors:  Kenneth Holmqvist; Saga Lee Örbom; Ignace T C Hooge; Diederick C Niehorster; Robert G Alexander; Richard Andersson; Jeroen S Benjamins; Pieter Blignaut; Anne-Marie Brouwer; Lewis L Chuang; Kirsten A Dalrymple; Denis Drieghe; Matt J Dunn; Ulrich Ettinger; Susann Fiedler; Tom Foulsham; Jos N van der Geest; Dan Witzner Hansen; Samuel B Hutton; Enkelejda Kasneci; Alan Kingstone; Paul C Knox; Ellen M Kok; Helena Lee; Joy Yeonjoo Lee; Jukka M Leppänen; Stephen Macknik; Päivi Majaranta; Susana Martinez-Conde; Antje Nuthmann; Marcus Nyström; Jacob L Orquin; Jorge Otero-Millan; Soon Young Park; Stanislav Popelka; Frank Proudlock; Frank Renkewitz; Austin Roorda; Michael Schulte-Mecklenbeck; Bonita Sharif; Frederick Shic; Mark Shovman; Mervyn G Thomas; Ward Venrooij; Raimondas Zemblys; Roy S Hessels
Journal:  Behav Res Methods       Date:  2022-04-06

4.  Expected-value bias in routine third-trimester growth scans.

Authors:  L Drukker; R Droste; P Chatelain; J A Noble; A T Papageorghiou
Journal:  Ultrasound Obstet Gynecol       Date:  2020-03       Impact factor: 7.299

5.  Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.

Authors:  Lior Drukker; Harshita Sharma; Richard Droste; Mohammad Alsharid; Pierre Chatelain; J Alison Noble; Aris T Papageorghiou
Journal:  Sci Rep       Date:  2021-07-08       Impact factor: 4.379

  5 in total

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