Dataset Information

Appearance of Maxwell's spot in images rendered using a cyan primary.

ABSTRACT: The discovery of melanopsin as a third type of retinal photoreceptor, contributing to both perceptual vision and reflex light responses, represents a new opportunity to optimise the design of artificial light sources for practical applications and to generate experimental stimuli. In the case of emissive displays, multiprimary designs incorporating a cyan primary could be used to allow melanopic radiance to be controlled independent of colour and luminance. Here we explore the performance a five-primary (violet, cyan, green, yellow, red) display device and find an anomaly in colour appearance when the cyan primary is employed. The anomaly took the form of a reddish/pinkish tinge in the central visual field, consistent with descriptions of Maxwell's spot. This effect was apparent in some full colour images and in uniform discs over a range of chromaticities. Its appearance in coloured discs correlated with differences in calculated colour coordinate between central and peripheral vision. A simulation indicated that inclusion of any primary with predominant output in the 470-500?nm range has the potential to produce such a discrepancy in central vs peripheral appearance. Applying an additional constraint in colour processing to reproduce naturally occurring differences in central vs peripheral colour coordinate eliminated appearance of the spot and produced acceptable colour images.

SUBMITTER: Gardasevic M

PROVIDER: S-EPMC6902267 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Appearance of Maxwell's spot in images rendered using a cyan primary.

Gardasevic Marina M Lucas Robert J RJ Allen Annette E AE

Vision research 20191031

The discovery of melanopsin as a third type of retinal photoreceptor, contributing to both perceptual vision and reflex light responses, represents a new opportunity to optimise the design of artificial light sources for practical applications and to generate experimental stimuli. In the case of emissive displays, multiprimary designs incorporating a cyan primary could be used to allow melanopic radiance to be controlled independent of colour and luminance. Here we explore the performance a five ...[more]

PMID: 31678617

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Project description:BackgroundMyopia is the leading cause of visual impairment and affects millions of children worldwide. Timely and annual manual optometric screenings of the entire at-risk population improve outcomes, but screening is challenging due to the lack of availability and training of assessors and the economic burden imposed by the screenings. Recently, deep learning and computer vision have shown powerful potential for disease screening. However, these techniques have not been applied to large-scale myopia screening using ocular appearance images.MethodsWe trained a deep learning system (DLS) for myopia detection using 2,350 ocular appearance images (processed by 7,050 pictures) from children aged 6 to 18. Myopia is defined as a spherical equivalent refraction (SER) [the algebraic sum in diopters (D), sphere + 1/2 cylinder] ≤-0.5 diopters. Saliency maps and gradient class activation maps (grad-CAM) were used to highlight the regions recognized by VGG-Face. In a prospective clinical trial, 100 ocular appearance images were used to assess the performance of the DLS.ResultsThe area under the curve (AUC), sensitivity, and specificity of the DLS were 0.9270 (95% CI, 0.8580-0.9610), 81.13% (95% CI, 76.86-5.39%), and 86.42% (95% CI, 82.30-90.54%), respectively. Based on the saliency maps and grad-CAMs, the DLS mainly focused on eyes, especially the temporal sclera, rather than the background or other parts of the face. In the prospective clinical trial, the DLS achieved better diagnostic performance than the ophthalmologists in terms of sensitivity [DLS: 84.00% (95% CI, 73.50-94.50%) versus ophthalmologists: 64.00% (95% CI, 48.00-72.00%)] and specificity [DLS: 74.00% (95% CI, 61.40-86.60%) versus ophthalmologists: 53.33% (95% CI, 30.00-66.00%)]. We also computed AUC subgroups stratified by sex and age. DLS achieved comparable AUCs for children of different sexes and ages.ConclusionsThis study for the first time applied deep learning to myopia screening using ocular images and achieved high screening accuracy, enabling the remote monitoring of the refractive status in children with myopia. The application of our DLS will directly benefit public health and relieve the substantial burden imposed by myopia-associated visual impairment or blindness.

Dataset Information

Appearance of Maxwell's spot in images rendered using a cyan primary.

Publications

Appearance of Maxwell's spot in images rendered using a cyan primary.

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