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      Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images

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          Abstract

          Introduction

          Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists’ gaze features.

          Purpose

          Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications.

          Method

          The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants’ focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants’ gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the “First Second” and full “Five Seconds” viewing duration.

          Results

          All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants’ gaze was more focused for “Five Seconds” than “First Second” data.

          Conclusions

          The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.

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          Most cited references34

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          Long-Term Storage Capacity of Reservoirs

          H E Hurst (1951)
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            Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations

            Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT) and links to the NBT tutorial website http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.
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              Holistic component of image perception in mammogram interpretation: gaze-tracking study.

              To test the hypothesis that rapid and accurate performance of the proficient observer in mammogram interpretation involves a shift in the mechanism of image perception from a relatively slow search-to-find mode to a relatively fast holistic mode. This HIPAA-compliant study had institutional review board approval, and participant informed consent was obtained; patient informed consent was not required. The eye positions of three full-time mammographers, one attending radiologist, two mammography fellows, and three radiology residents were recorded during the interpretation of 20 normal and 20 subtly abnormal mammograms. The search time required to first locate a cancer, as well as the initial eye scan path, was determined and compared with diagnostic performance as measured with receiver operating characteristic (ROC) analysis. The median time for all observers to fixate a cancer, regardless of the decision outcome, was 1.13 seconds, with a range of 0.68 second to 3.06 seconds. Even though most of the lesions were fixated, recognition of them as cancerous ranged from 85% (17 of 20) to 10% (two of 20), with corresponding areas under the ROC curve of 0.87-0.40. The ROC index of detectability, d(a), was linearly related to the time to first fixate a cancer with a correlation (r(2)) of 0.81. The rapid initial fixation of a true abnormality is evidence for a global perceptual process capable of analyzing the visual input of the entire retinal image and pinpointing the spatial location of an abnormality. It appears to be more highly developed in the most proficient observers, replacing the less efficient initial search-to-find strategies. (c) RSNA, 2007.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draft
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: Validation
                Role: Funding acquisitionRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Validation
                Role: VisualizationRole: Writing – review & editing
                Role: Methodology
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2021
                2 December 2021
                : 16
                : 12
                Affiliations
                [1 ] Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
                [2 ] School of Psychological Sciences, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
                [3 ] Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
                [4 ] Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Australia
                UMR8194, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-21-21363
                10.1371/journal.pone.0260717
                8639086
                0d318985-ed01-4b29-8305-d843ad41c02b
                © 2021 Suman et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 8, Tables: 1, Pages: 19
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001230, macquarie university;
                Award ID: 080619
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000923, australian research council;
                Award ID: FT190100623
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001230, macquarie university;
                Award ID: 080619
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001230, macquarie university;
                Award ID: 080619
                Award Recipient :
                This work was funded by the Centre for Elite Performance, Expertise & Training (Macquarie University, Sydney) Seeding Grant awarded to Dr Patrick Nalepka, Dr Ann Carrigan, & Prof. Antonio Di Ieva in 2019 (080619) and by an Australian Research Council (ARC) Future Fellowship granted to Prof. Antonio Di Ieva in 2019 (FT190100623). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Custom metadata
                All relevant data files and code are available from the following Github public repository link https://github.com/Abdulla-Al-Suman/Eye-Tracking-on-Brain-MRI.

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