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Reddingius P, Asfaw DS, Mönter VM, Smith ND, Jones PR, Crabb DP. Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing. Data Brief 2023; 48:109184. [PMID: 37234734 PMCID: PMC10206150 DOI: 10.1016/j.dib.2023.109184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
This paper describes data from Asfaw at al. [1], which examined the eye movements of glaucoma patients (n=15) with pronounced asymmetrical vision loss (visual field loss worse in one eye). This allows for within-subject comparisons between the better and worse eye, thereby controlling for the effects of individual differences between patients. All patients had a clinical diagnosis of open angle glaucoma (OAG). Participants were asked to look at images of nature monocularly (free viewing; fellow eye patched) while gaze was recorded at 1000 Hz using a remote eye tracker (EyeLink 1000). Raw and processed eye tracking data are provided. In addition, clinical (visual acuity, contrast sensitivity and visual field) and demographic information (age, sex) are provided.
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Jones L, Callaghan T, Campbell P, Jones PR, Taylor DJ, Asfaw DS, Edgar DF, Crabb DP. Acceptability of a home-based visual field test (Eyecatcher) for glaucoma home monitoring: a qualitative study of patients' views and experiences. BMJ Open 2021; 11:e043130. [PMID: 33820785 PMCID: PMC8030466 DOI: 10.1136/bmjopen-2020-043130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 01/25/2021] [Accepted: 02/05/2021] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVES To explore the acceptability of home visual field (VF) testing using Eyecatcher among people with glaucoma participating in a 6-month home monitoring pilot study. DESIGN Qualitative study using face-to-face semistructured interviews. Transcripts were analysed using thematic analysis. SETTING Participants were recruited in the UK through an advertisement in the International Glaucoma Association (now Glaucoma UK) newsletter. PARTICIPANTS Twenty adults (10 women; median age: 71 years) with a diagnosis of glaucoma were recruited (including open angle and normal tension glaucoma; mean deviation=2.5 to -29.9 dB). RESULTS All participants could successfully perform VF testing at home. Interview data were coded into four overarching themes regarding experiences of undertaking VF home monitoring and attitudes towards its wider implementation in healthcare: (1) comparisons between Eyecatcher and Humphrey Field Analyser (HFA); (2) capability using Eyecatcher; (3) practicalities for effective wider scale implementation; (4) motivations for home monitoring. CONCLUSIONS Participants identified a broad range of benefits to VF home monitoring and discussed areas for service improvement. Eyecatcher was compared positively with conventional VF testing using HFA. Home monitoring may be acceptable to at least a subset of people with glaucoma.
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Affiliation(s)
- Lee Jones
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Tamsin Callaghan
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Peter Campbell
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
- Department of Ophthalmology, Guy's and St Thomas' Hospitals NHS Trust, London, UK
| | - Pete R Jones
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Deanna J Taylor
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Daniel S Asfaw
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - David F Edgar
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - David P Crabb
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
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Jones PR, Campbell P, Callaghan T, Jones L, Asfaw DS, Edgar DF, Crabb DP. Glaucoma Home Monitoring Using a Tablet-Based Visual Field Test (Eyecatcher): An Assessment of Accuracy and Adherence Over 6 Months. Am J Ophthalmol 2021; 223:42-52. [PMID: 32882222 PMCID: PMC7462567 DOI: 10.1016/j.ajo.2020.08.039] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 01/14/2023]
Abstract
Purpose To assess accuracy and adherence of visual field (VF) home monitoring in a pilot sample of patients with glaucoma. Design Prospective longitudinal feasibility and reliability study. Methods Twenty adults (median 71 years) with an established diagnosis of glaucoma were issued a tablet perimeter (Eyecatcher) and were asked to perform 1 VF home assessment per eye, per month, for 6 months (12 tests total). Before and after home monitoring, 2 VF assessments were performed in clinic using standard automated perimetry (4 tests total, per eye). Results All 20 participants could perform monthly home monitoring, though 1 participant stopped after 4 months (adherence: 98% of tests). There was good concordance between VFs measured at home and in the clinic (r = 0.94, P < .001). In 21 of 236 tests (9%), mean deviation deviated by more than ±3 dB from the median. Many of these anomalous tests could be identified by applying machine learning techniques to recordings from the tablets' front-facing camera (area under the receiver operating characteristic curve = 0.78). Adding home-monitoring data to 2 standard automated perimetry tests made 6 months apart reduced measurement error (between-test measurement variability) in 97% of eyes, with mean absolute error more than halving in 90% of eyes. Median test duration was 4.5 minutes (quartiles: 3.9-5.2 minutes). Substantial variations in ambient illumination had no observable effect on VF measurements (r = 0.07, P = .320). Conclusions Home monitoring of VFs is viable for some patients and may provide clinically useful data.
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Affiliation(s)
- Pete R Jones
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Peter Campbell
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK; Department of Ophthalmology, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Tamsin Callaghan
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Lee Jones
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Daniel S Asfaw
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - David F Edgar
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - David P Crabb
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
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Jones PR, Demaria G, Tigchelaar I, Asfaw DS, Edgar DF, Campbell P, Callaghan T, Crabb DP. The Human Touch: Using a Webcam to Autonomously Monitor Compliance During Visual Field Assessments. Transl Vis Sci Technol 2020; 9:31. [PMID: 32855877 PMCID: PMC7422775 DOI: 10.1167/tvst.9.8.31] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 03/16/2020] [Indexed: 12/03/2022] Open
Abstract
Purpose To explore the feasibility of using various easy-to-obtain biomarkers to monitor non-compliance (measurement error) during visual field assessments. Methods Forty-two healthy adults (42 eyes) and seven glaucoma patients (14 eyes) underwent two same-day visual field assessments. An ordinary webcam was used to compute seven potential biomarkers of task compliance, based primarily on eye gaze, head pose, and facial expression. We quantified the association between each biomarker and measurement error, as defined by (1) test–retest differences in overall test scores (mean sensitivity), and (2) failures to respond to visible stimuli on individual trials (stimuli −3 dB or more brighter than threshold). Results In healthy eyes, three of the seven biomarkers were significantly associated with overall (test–retest) measurement error (P = 0.003–0.007), and at least two others exhibited possible trends (P = 0.052–0.060). The weighted linear sum of all seven biomarkers was associated with overall measurement error, in both healthy eyes (r = 0.51, P < 0.001) and patients (r = 0.65, P < 0.001). Five biomarkers were each associated with failures to respond to visible stimuli on individual trials (all P < 0.001). Conclusions Inexpensive, autonomous measures of task compliance are associated with measurement error in visual field assessments, in terms of both the overall reliability of a test and failures to respond on particular trials (“lapses”). This could be helpful for identifying low-quality assessments and for improving assessment techniques (e.g., by discounting suspect responses or by automatically triggering comfort breaks or encouragement). Translational Relevance This study explores a potential way of improving the reliability of visual field assessments, a crucial but notoriously unreliable clinical measure.
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Affiliation(s)
- Pete R Jones
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Giorgia Demaria
- Department of Ophthalmology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Graduate School of Medical Sciences (Research School of Behavioral and Cognitive Neurosciences), University of Groningen, Groningen, The Netherlands
| | - Iris Tigchelaar
- Department of Ophthalmology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Ocusweep, Turku, Finland.,Doctoral Program in Clinical Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Daniel S Asfaw
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - David F Edgar
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - Peter Campbell
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK.,Department of Ophthalmology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Tamsin Callaghan
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
| | - David P Crabb
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, UK
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Asfaw DS, Jones PR, Edwards LA, Smith ND, Crabb DP. Using eye movements to detect visual field loss: a pragmatic assessment using simulated scotoma. Sci Rep 2020; 10:9782. [PMID: 32555198 PMCID: PMC7299979 DOI: 10.1038/s41598-020-66196-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 05/15/2020] [Indexed: 12/02/2022] Open
Abstract
Glaucoma is a leading cause of irreversible sight-loss and has been shown to affect natural eye-movements. These changes may provide a cheap and easy-to-obtain biomarker for improving disease detection. Here, we investigated whether these changes are large enough to be clinically useful. We used a gaze-contingent simulated visual field (VF) loss paradigm, in which participants experienced a variable magnitude of simulated VF loss based on longitudinal data from a real glaucoma patient (thereby controlling for other variables, such as age and general health). Fifty-five young participants with healthy vision were asked to view two short videos and three pictures, either with: (1) no VF loss, (2) moderate VF loss, or (3) advanced VF loss. Eye-movements were recorded using a remote eye tracker. Key eye-movement parameters were computed, including saccade amplitude, the spread of saccade endpoints (bivariate contour ellipse area), location of saccade landing positions, and similarity of fixations locations among participants (quantified using kernel density estimation). The simulated VF loss caused some statistically significant effects in the eye movement parameters. Yet, these effects were not capable of consistently identifying simulated VF loss, despite it being of a magnitude likely easily detectable by standard automated perimetry.
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Affiliation(s)
- Daniel S Asfaw
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, EC1V 0HB, UK
| | - Pete R Jones
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, EC1V 0HB, UK
| | - Laura A Edwards
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, EC1V 0HB, UK
| | - Nicholas D Smith
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, EC1V 0HB, UK
| | - David P Crabb
- Division of Optometry and Visual Sciences, School of Health Sciences, City, University of London, London, EC1V 0HB, UK.
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Asfaw DS, Jones PR, Mönter VM, Smith ND, Crabb DP. Does Glaucoma Alter Eye Movements When Viewing Images of Natural Scenes? A Between-Eye Study. Invest Ophthalmol Vis Sci 2019; 59:3189-3198. [PMID: 29971443 DOI: 10.1167/iovs.18-23779] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To investigate whether glaucoma produces measurable changes in eye movements. Methods Fifteen glaucoma patients with asymmetric vision loss (difference in mean deviation [MD] > 6 dB between eyes) were asked to monocularly view 120 images of natural scenes, presented sequentially on a computer monitor. Each image was viewed twice-once each with the better and worse eye. Patients' eye movements were recorded with an Eyelink 1000 eye-tracker. Eye-movement parameters were computed and compared within participants (better eye versus worse eye). These parameters included a novel measure: saccadic reversal rate (SRR), as well as more traditional metrics such as saccade amplitude, fixation counts, fixation duration, and spread of fixation locations (bivariate contour ellipse area [BCEA]). In addition, the associations of these parameters with clinical measures of vision were investigated. Results In the worse eye, saccade amplitude\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\unicode[Times]{x1D6C2}}\)\(\def\bupbeta{\unicode[Times]{x1D6C3}}\)\(\def\bupgamma{\unicode[Times]{x1D6C4}}\)\(\def\bupdelta{\unicode[Times]{x1D6C5}}\)\(\def\bupepsilon{\unicode[Times]{x1D6C6}}\)\(\def\bupvarepsilon{\unicode[Times]{x1D6DC}}\)\(\def\bupzeta{\unicode[Times]{x1D6C7}}\)\(\def\bupeta{\unicode[Times]{x1D6C8}}\)\(\def\buptheta{\unicode[Times]{x1D6C9}}\)\(\def\bupiota{\unicode[Times]{x1D6CA}}\)\(\def\bupkappa{\unicode[Times]{x1D6CB}}\)\(\def\buplambda{\unicode[Times]{x1D6CC}}\)\(\def\bupmu{\unicode[Times]{x1D6CD}}\)\(\def\bupnu{\unicode[Times]{x1D6CE}}\)\(\def\bupxi{\unicode[Times]{x1D6CF}}\)\(\def\bupomicron{\unicode[Times]{x1D6D0}}\)\(\def\buppi{\unicode[Times]{x1D6D1}}\)\(\def\buprho{\unicode[Times]{x1D6D2}}\)\(\def\bupsigma{\unicode[Times]{x1D6D4}}\)\(\def\buptau{\unicode[Times]{x1D6D5}}\)\(\def\bupupsilon{\unicode[Times]{x1D6D6}}\)\(\def\bupphi{\unicode[Times]{x1D6D7}}\)\(\def\bupchi{\unicode[Times]{x1D6D8}}\)\(\def\buppsy{\unicode[Times]{x1D6D9}}\)\(\def\bupomega{\unicode[Times]{x1D6DA}}\)\(\def\bupvartheta{\unicode[Times]{x1D6DD}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bUpsilon{\bf{\Upsilon}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\(\def\iGamma{\unicode[Times]{x1D6E4}}\)\(\def\iDelta{\unicode[Times]{x1D6E5}}\)\(\def\iTheta{\unicode[Times]{x1D6E9}}\)\(\def\iLambda{\unicode[Times]{x1D6EC}}\)\(\def\iXi{\unicode[Times]{x1D6EF}}\)\(\def\iPi{\unicode[Times]{x1D6F1}}\)\(\def\iSigma{\unicode[Times]{x1D6F4}}\)\(\def\iUpsilon{\unicode[Times]{x1D6F6}}\)\(\def\iPhi{\unicode[Times]{x1D6F7}}\)\(\def\iPsi{\unicode[Times]{x1D6F9}}\)\(\def\iOmega{\unicode[Times]{x1D6FA}}\)\(\def\biGamma{\unicode[Times]{x1D71E}}\)\(\def\biDelta{\unicode[Times]{x1D71F}}\)\(\def\biTheta{\unicode[Times]{x1D723}}\)\(\def\biLambda{\unicode[Times]{x1D726}}\)\(\def\biXi{\unicode[Times]{x1D729}}\)\(\def\biPi{\unicode[Times]{x1D72B}}\)\(\def\biSigma{\unicode[Times]{x1D72E}}\)\(\def\biUpsilon{\unicode[Times]{x1D730}}\)\(\def\biPhi{\unicode[Times]{x1D731}}\)\(\def\biPsi{\unicode[Times]{x1D733}}\)\(\def\biOmega{\unicode[Times]{x1D734}}\)\((P = 0.012; - 13\% \)) and BCEA \((P = 0.005; - 16\% )\) were smaller, while SRR was greater (\(P = 0.018; + 16\% \)). There was a significant correlation between the intereye difference in BCEA, and differences in MD values (\({\rm{Spearman^{\prime} s}}\ r = 0.65;P = 0.01\)), while differences in SRR were associated with differences in visual acuity (\({\rm{Spearman^{\prime} s}}\ r = 0.64;P = 0.01\)). Furthermore, between-eye differences in BCEA were a significant predictor of between-eye differences in MD: for every 1-dB difference in MD, BCEA reduced by 6.2% (95% confidence interval, 1.6%-10.3%). Conclusions Eye movements are altered by visual field loss, and these changes are related to changes in clinical measures. Eye movements recorded while passively viewing images could potentially be used as biomarkers for visual field damage.
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Affiliation(s)
- Daniel S Asfaw
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, United Kingdom
| | - Pete R Jones
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, United Kingdom
| | - Vera M Mönter
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, United Kingdom
| | - Nicholas D Smith
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, United Kingdom
| | - David P Crabb
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, United Kingdom
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Bernal J, Kushibar K, Asfaw DS, Valverde S, Oliver A, Martí R, Lladó X. Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 2018; 95:64-81. [PMID: 30195984 DOI: 10.1016/j.artmed.2018.08.008] [Citation(s) in RCA: 119] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 04/25/2018] [Accepted: 08/27/2018] [Indexed: 02/07/2023]
Abstract
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.
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Affiliation(s)
- Jose Bernal
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
| | - Kaisar Kushibar
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
| | - Daniel S Asfaw
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
| | - Sergi Valverde
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
| | - Arnau Oliver
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
| | - Robert Martí
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
| | - Xavier Lladó
- Computer Vision and Robotics Institute, Dept. of Computer Architecture and Technology, University of Girona, Ed. P-IV, Av. Lluis Santaló s/n, 17003 Girona, Spain.
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Asfaw DS, Jones PR, Smith ND, Crabb DP. Data on eye movements in people with glaucoma and peers with normal vision. Data Brief 2018; 19:1266-1273. [PMID: 29922707 PMCID: PMC6005790 DOI: 10.1016/j.dib.2018.05.076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 05/15/2018] [Indexed: 11/28/2022] Open
Abstract
Eye movements of glaucoma patients have been shown to differ from age-similar control groups when performing everyday tasks, such as reading (Burton et al., 2012; Smith et al., 2014) [1], [2], visual search (Smith et al., 2012) [3], face recognition (Glen et al., 2013) [4], driving, and viewing static images (Smith et al., 2012) [5]. Described here is the dataset from a recent publication in which we compared the eye-movements of 44 glaucoma patients and 32 age-similar controls, while they watched a series of short video clips taken from television programs (Crabb et al., 2018) [6]. Gaze was recorded at 1000 Hz using a remote eye-tracker. We also provide demographic information and results from a clinical examination of vision for each participant.
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Affiliation(s)
- Daniel S Asfaw
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, EC1V 0HB, UK
| | - Pete R Jones
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, EC1V 0HB, UK
| | - Nicholas D Smith
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, EC1V 0HB, UK
| | - David P Crabb
- Division of Optometry and Visual Science, School of Health Science, City, University of London, London, EC1V 0HB, UK
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