Data on OCT and fundus images for the detection of glaucoma.
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ABSTRACT:
SUBMITTER: Raja H
PROVIDER: S-EPMC7062936 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Data on OCT and fundus images for the detection of glaucoma.
Raja Hina H Akram M Usman MU Khawaja Sajid Gul SG Arslan Muhammad M Ramzan Aneeqa A Nazir Noman N
Data in brief 20200228
This paper presents the data set of Optic coherence tomography (OCT) and fundus Images of human eye. The OCT machine TOPCON'S 3D OCT-1000 camera is employed to acquire the images. The dataset is comprised of 50 images which includes control and glaucomatous images. For each OCT Image there is a corresponding fundus Image with annotation. Cup to disc ratio (CDR) values annotated by glaucoma specialists through fundus Images are provided in excel file. OCT images are optic nerve head (ONH) centred ...[more]
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Project description:To test the hypothesis that vertical asymmetry in macular ganglion cell/inner plexiform layer (GCIPL) thickness can improve detection of early glaucoma.Sixty-nine normal eyes and 101 glaucoma eyes had macular imaging with spectral-domain optical coherence tomography (OCT; 200 × 200 cube). The resulting GCIPL thickness matrix was grouped into a 20 × 20 superpixel array and superior superpixels were compared to their inferior counterparts. A global asymmetry index (AI) was defined as the grand mean of the asymmetry ratios. To measure local asymmetry, the corresponding thickness measurements of three rows above and below the horizontal raphe were compared individually and in combinations. Global and local AIs were compared to the best-performing GCIPL thickness parameters with area under the receiver operating curves (AUC) and sensitivity/specificities.Age or axial length did not influence AIs in normal subjects (P ≥ 0.08). Global and local AIs were significantly higher in the glaucoma group compared to normal eyes. Minimum (AUC = 0.962, 95% confidence interval [CI]: 0.936-0.989) and inferotemporal thickness (AUC = 0.944, 95% CI: 0.910-0.977; P = 0.122) performed best for detection of early glaucoma. The AUC for global AI was 0.851 (95% CI: 0.792-0.909) compared to 0.916 (95% CI: 0.874-0.958) for the best local AI. Combining minimum or inferotemporal GCIPL thickness and the best local AI led to higher partial AUCs (0.088 and 0.085, 90% specificity, P = 0.120 and 0.130, respectively) than GCIPL thickness measures.Macular vertical thickness asymmetry measures did not perform better than sectoral or minimum GCIPL thickness for detection of early glaucoma. Combining local asymmetry parameters with the best sectoral GCIPL thickness measures enhanced this task.
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Project description:PurposeTo determine if OCT angiography (OCTA)-derived vessel density measurements can extend the available dynamic range for detecting glaucoma compared with spectral-domain (SD) OCT-derived thickness measurements.DesignObservational, cross-sectional study.ParticipantsA total of 509 eyes from 38 healthy participants, 63 glaucoma suspects, and 193 glaucoma patients enrolled in the Diagnostic Innovations in Glaucoma Study.MethodsRelative vessel density and tissue thickness measurement floors of perifoveal vessel density (pfVD), circumpapillary capillary density (cpCD), circumpapillary retinal nerve fiber (cpRNFL) thickness, ganglion cell complex (GCC) thickness, and visual field (VF) mean deviation (MD) were investigated and compared with a previously reported linear change point model (CPM) and locally weighted scatterplot smoothing curves.Main outcome measuresEstimated vessel density and tissue thickness measurement floors and corresponding dynamic ranges.ResultsVisual field MD ranged from -30.1 to 2.8 decibels (dB). No measurement floor was found for pfVD, which continued to decrease constantly until very advanced disease. A true floor (i.e., slope of approximately 0 after observed CPM change point) was detected for cpRNFL thickness only. The post-CPM estimated floors were 49.5±2.6 μm for cpRNFL thickness, 70.7±1.0 μm for GCC thickness, and 31.2±1.1% for cpCD. Perifoveal vessel density reached the post-CPM estimated floor later in the disease (VF MD, -25.8±3.8 dB) than cpCD (VF MD, -19.3±2.4 dB), cpRNFL thickness (VF MD, -17.5±3.3 dB), and GCC thickness (VF MD, -13.9±1.8 dB; P < 0.001). The number of available measurement steps from normal values to the CPM estimated floor was greatest for cpRNFL thickness (8.9), followed by GCC thickness (7.4), cpCD (4.5), and pfVD (3.8).ConclusionsIn late-stage glaucoma, particularly when VF MD is worse than -14 dB, OCTA-measured pfVD is a promising tool for monitoring progression because it does not have a detectable measurement floor. However, the number of steps within the dynamic range of a parameter also needs to be considered. Although thickness parameters reached the floor earlier than OCTA-measured pfVD, there are more such steps with thickness than OCTA parameters.
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Project description:BackgroundRetinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screening on a large scale is time-consuming and labour-intensive. Here, we aim to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images.MethodsA total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The Zhongshan Ophthalmic Center (ZOC) dataset was selected to compare the performance of the system to that of retina specialists in RED detection. The saliency map visualization technique was used to understand which areas in the UWF image had the most influence on our deep learning system when detecting RED.ResultsThe system for RED detection achieved areas under the receiver operating characteristic curve of 0.994 (95% confidence interval [CI]: 0.991-0.996), 0.972 (95% CI: 0.957-0.984), and 0.988 (95% CI: 0.983-0.992) in three independent datasets. The performance of the system in the ZOC dataset was comparable to that of an experienced retina specialist. Regions of RED were highlighted by saliency maps in UWF images.ConclusionsOur deep learning system is reliable in the automated detection of RED in UWF images. As a screening tool, our system may promote the early diagnosis and management of RED-related fundus diseases.
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