A Comparison between the Compass Fundus Perimeter and the Humphrey Field Analyzer

Montesano, Giovanni; Bryan, Susan R; Crabb, David P.; Fogagnolo, Paolo; Oddone, Francesco; McKendrick, Allison M; Turpin, Andrew; Lanzetta, Paolo; Perdicchi, Andrea; Johnson, Chris A.; Garway-Heath, David; Brusini, Paolo; Rossetti, Luca

doi:10.1016/j.ophtha.2018.08.010

Cited by 46 publications

(62 citation statements)

References 29 publications

(36 reference statements)

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“…Our model increases feature correlation and expression learning, exploiting the horizontally-layered retinal structure and the biological knowledge that retinal surfaces can be modeled as partitioned layers along the vertical dimension. We showcase the diagnostic precision and agreement of our method with ground truth RNFL (commonly assessed layer) segmen-tations from two independent studies [1,15]. Finally, we demonstrate the superiority of our method in segmenting all retinal layers using the Duke dataset [16].…”

Section: Introductionmentioning

confidence: 66%

“…We used two clinical studies, COMPASS [15] and RAPID [1], and the publicly available Duke dataset [3] to evaluate our proposed methodology, conducting RAPID study The RAPID study consists of 82 stable glaucoma patients attended Moorfields Eye Hospital for up to 10 visits within a 3-month period, consisting of 502 SDOCT (SpectralisOCT, Heidelberg Engineering) images. We split the RAPID study into training, validation and testing images [1].…”

Section: Datamentioning

confidence: 99%

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Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Lazaridis

Afgeh

et al. 2020

Ophthalmic Medical Image Analysis

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Albeit optical coherence imaging (OCT) is widely used to assess ophthalmic pathologies, localization of intra-retinal boundaries suffers from erroneous segmentations due to image artifacts or topological abnormalities. Although deep learning-based methods have been effectively applied in OCT imaging, accurate automated layer segmentation remains a challenging task, with the flexibility and precision of most methods being highly constrained. In this paper, we propose a novel method to segment all retinal layers, tailored to the bio-topological OCT geometry. In addition to traditional learning of shift-invariant features, our method learns in selected pixels horizontally and vertically, exploiting the orientation of the extracted features. In this way, the most discriminative retinal features are generated in a robust manner, while long-range pixel dependencies across spatial locations are efficiently captured. To validate the effectiveness and generalisation of our method, we implement three sets of networks based on different backbone models. Results on three independent studies show that our methodology consistently produces more accurate segmentations than state-of-the-art networks, and shows better precision and agreement with ground truth. Thus, our method not only improves segmentation, but also enhances the statistical power of clinical trials with layer thickness change outcomes.

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Section: Introductionmentioning

confidence: 66%

Section: Datamentioning

confidence: 99%

Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Lazaridis

Afgeh

et al. 2020

Ophthalmic Medical Image Analysis

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“…The model for the macular sensitivities was estimated using a large collection of visual field tests from 444 healthy subjects recruited for the validation study for the Compass. 18 These subjects were examined with a grid that contained all the points of a 24-2 grid with 12 additional macular points. A mixed model with eccentricity and age as predictors was used to estimate the normal sensitivity for the tested locations.…”

Section: Methodsmentioning

confidence: 99%

“…To improve the accuracy of our model, we used a fundus perimeter employing an implementation of the ZEST as the standard testing strategy and equipped with scanning laser ophthalmoscopy (SLO) tracking to acquire measurements from healthy subjects and patients with glaucoma. 16–18 This allowed precise localization of the stimuli on the structural maps. Furthermore, instead of using the final thresholds from the tests, we built our model based on subjects' binary responses (seen or not seen) to each stimulus projected during the test.…”

Section: Introductionmentioning

confidence: 99%

Improving Visual Field Examination of the Macula Using Structural Information

Montesano

Rossetti

Allegrini

et al. 2018

Trans. Vis. Sci. Tech.

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PurposeTo investigate a novel approach for structure-function modeling in glaucoma to improve visual field testing in the macula.MethodsWe acquired data from the macular region in 20 healthy eyes and 31 with central glaucomatous damage. Optical coherence tomography (OCT) scans were used to estimate the local macular ganglion cell density. Perimetry was performed with a fundus-tracking device using a 10-2 grid. OCT scans were matched to the retinal image from the fundus perimeter to accurately map the tested locations onto the structural damage. Binary responses from the subjects to all presented stimuli were used to calculate the structure-function model used to generate prior distributions for a ZEST (Zippy Estimation by Sequential Testing) Bayesian strategy. We used simulations based on structural and functional data acquired from an independent dataset of 20 glaucoma patients to compare the performance of this new strategy, structural macular ZEST (MacS-ZEST), with a standard ZEST.ResultsCompared to the standard ZEST, MacS-ZEST reduced the number of presentations by 13% in reliable simulated subjects and 14% with higher rates (≥20%) of false positive or false negative errors. Reduction in mean absolute error was not present for reliable subjects but was gradually more important with unreliable responses (≥10% at 30% error rate).ConclusionsBinary responses can be modeled to incorporate detailed structural information from macular OCT into visual field testing, improving overall speed and accuracy in poor responders.Translational RelevanceStructural information can improve speed and reliability for macular testing in glaucoma practice.

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“…12,13 Only one commercial microperimeter, the Compass (CenterVue, Padova, Italy), uses the Total and Pattern Deviation probability analyses. 14 The remaining microperimeters represent the measured sensitivity at each stimulus location by a continuous scale of color designation. The color designation represents the absolute value of sensitivity, but the normal value, to which the sensitivity is referenced, varies as a function of eccentricity and of age.…”

Section: Introductionmentioning

confidence: 99%

Microperimetry in Age-Related Macular Degeneration: An Evidence-Base for Pattern Deviation Probability Analysis in Microperimetry

Cassels

Wild

Margrain

et al. 2019

Trans. Vis. Sci. Tech.

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Purpose: The ''traffic light'' color designation of differential light sensitivity used in a number of microperimeters does not encompass the conventional Total and Pattern Deviation probability analyses adopted by standard automated perimetry. We determined whether the color designation is indicative of abnormality as represented by the ''gold standard'' Pattern Deviation probability analysis. Methods: Total and Pattern Deviation probability levels, using two different methods, were derived at each of 40 stimulus locations, within 78 eccentricity, from 66 ocular healthy individuals (66 eyes) who had undergone microperimetry with the Macular Integrity Assessment microperimeter. The probability levels were applied to the corresponding fields from each of 45 individuals (45 eyes) with age-related macular degeneration (AMD) and evaluated in relation to the color designation. Results: Sensitivities designated in orange encompassed the entire range of Pattern Deviation probability levels (from normal to P 1%). Those designated in green were mostly normal; those in red/black generally corresponded to the 1% probability level. Conclusions: The green and the red/black designations are generally indicative of normal and abnormal probability values, respectively. The orange designation encompassed all probability outcomes and should not be relied upon for visual field interpretation. The evidence base indicates replacement of the color designation of sensitivity in AMD by Total Deviation and Pattern Deviation analyses. Translational Relevance: The use of Total and Pattern Deviation probability analyses is not universal in all microperimeters, and the derivation of these values indicates that color coding will lead to errors in evaluating visual field loss.

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A Comparison between the Compass Fundus Perimeter and the Humphrey Field Analyzer

Abstract: Relative diagnostic precision of the 2 devices is equivalent. Test-retest variability of MS for CMP was better than for HFA.

Cited by 46 publications

References 29 publications

Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Improving Visual Field Examination of the Macula Using Structural Information

Microperimetry in Age-Related Macular Degeneration: An Evidence-Base for Pattern Deviation Probability Analysis in Microperimetry

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