Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

Expansion of retinal nerve fiber bundle narrowing in glaucoma: An adaptive optics scanning laser ophthalmoscopy study

Purpose: To investigate longitudinal changes in the retinal nerve fiber bundle in eyes with primary open angle glaucoma using adaptive optics scanning laser ophthalmoscopy. Methods: A prospective observational case series. Fourteen eyes from 12 patients with primary open angle glaucoma that exhibited retinal nerve fiber layer defects on fundus photography were imaged with adaptive optics scanning laser ophthalmoscopy over time. Results: The expansion of retinal nerve fiber bundle narrowing was observed on adaptive optics scanning laser ophthalmoscopy in 8 eyes (57.1%) over a period of 1.44 ± 0.42 years. Retinal nerve fiber bundle narrowing expanded horizontally in 2 eyes and vertically in 6 eyes. In 3 eyes, changes in the retinal nerve fiber layer were only detectable on adaptive optics scanning laser ophthalmoscopy images. Conclusions and Importance: The expansion of retinal nerve fiber bundle narrowing was observed using adaptive optics scanning laser ophthalmoscopy. Accordingly, this tool may be a useful tool for detecting glaucoma-related changes in retinal nerve fibers in a short time

Peripapillary and macular choroidal thickness in glaucoma.

PurposeTo compare choroidal thickness (CT) between individuals with and without glaucomatous damage and to explore the association of peripapillary and submacular CT with glaucoma severity using spectral domain optical coherence tomography (SD-OCT).MethodsNinety-one eyes of 20 normal subjects and 43 glaucoma patients from the UCLA SD-OCT Imaging Study were enrolled. Imaging was performed using Cirrus HD-OCT. Choroidal thickness was measured at four predetermined points in the macular and peripapillary regions, and compared between glaucoma and control groups before and after adjusting for potential confounding variables.ResultsThe average (± standard deviation) mean deviation (MD) on visual fields was -0.3 (±2.0) dB in controls and -3.5 (±3.5) dB in glaucoma patients. Age, axial length and their interaction were the most significant factors affecting CT on multivariate analysis. Adjusted average CT (corrected for age, axial length, their interaction, gender and lens status) however, was not different between glaucoma patients and the control group (P=0.083) except in the temporal parafoveal region (P=0.037); nor was choroidal thickness related to glaucoma severity (r=-0.187, P=0.176 for correlation with MD, r=-0.151, P=0.275 for correlation with average nerve fiber layer thickness).ConclusionsChoroidal thickness of the macular and peripapillary regions is not decreased in glaucoma. Anatomical measurements with SD-OCT do not support the possible influence of the choroid on the pathophysiology of glaucoma

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd

Machine Learning Approaches for Automated Glaucoma Detection using Clinical Data and Optical Coherence Tomography Images

Glaucoma is a multi-factorial, progressive blinding optic-neuropathy. A variety of factors, including genetics, vasculature, anatomy, and immune factors, are involved. Worldwide more than 80 million people are affected by glaucoma, and around 300,000 in Australia, where 50% remain undiagnosed. Untreated glaucoma can lead to blindness. Early detection by Artificial intelligence (AI) is crucial to accelerate the diagnosis process and can prevent further vision loss. Many proposed AI systems have shown promising performance for automated glaucoma detection using two-dimensional (2D) data. However, only a few studies had optimistic outcomes for glaucoma detection and staging. Moreover, the automated AI system still faces challenges in diagnosing at the clinicians’ level due to the lack of interpretability of the ML algorithms and integration of multiple clinical data. AI technology would be welcomed by doctors and patients if the "black box" notion is overcome by developing an explainable, transparent AI system with similar pathological markers used by clinicians as the sign of early detection and progression of glaucomatous damage. Therefore, the thesis aimed to develop a comprehensive AI model to detect and stage glaucoma by incorporating a variety of clinical data and utilising advanced data analysis and machine learning (ML) techniques. The research first focuses on optimising glaucoma diagnostic features by combining structural, functional, demographic, risk factor, and optical coherence tomography (OCT) features. The significant features were evaluated using statistical analysis and trained in ML algorithms to observe the detection performance. Three crucial structural ONH OCT features: cross-sectional 2D radial B-scan, 3D vascular angiography and temporal-superior-nasal-inferior-temporal (TSNIT) B-scan, were analysed and trained in explainable deep learning (DL) models for automated glaucoma prediction. The explanation behind the decision making of DL models were successfully demonstrated using the feature visualisation. The structural features or distinguished affected regions of TSNIT OCT scans were precisely localised for glaucoma patients. This is consistent with the concept of explainable DL, which refers to the idea of making the decision-making processes of DL models transparent and interpretable to humans. However, artifacts and speckle noise often result in misinterpretation of the TSNIT OCT scans. This research also developed an automated DL model to remove the artifacts and noise from the OCT scans, facilitating error-free retinal layers segmentation, accurate tissue thickness estimation and image interpretation. Moreover, to monitor and grade glaucoma severity, the visual field (VF) test is commonly followed by clinicians for treatment and management. Therefore, this research uses the functional features extracted from VF images to train ML algorithms for staging glaucoma from early to advanced/severe stages. Finally, the selected significant features were used to design and develop a comprehensive AI model to detect and grade glaucoma stages based on the data quantity and availability. In the first stage, a DL model was trained with TSNIT OCT scans, and its output was combined with significant structural and functional features and trained in ML models. The best-performed ML model achieved an area under the curve (AUC): 0.98, an accuracy of 97.2%, a sensitivity of 97.9%, and a specificity of 96.4% for detecting glaucoma. The model achieved an overall accuracy of 90.7% and an F1 score of 84.0% for classifying normal, early, moderate, and advanced-stage glaucoma. In conclusion, this thesis developed and proposed a comprehensive, evidence-based AI model that will solve the screening problem for large populations and relieve experts from manually analysing a slew of patient data and associated misinterpretation problems. Moreover, this thesis demonstrated three structural OCT features that could be added as excellent diagnostic markers for precise glaucoma diagnosis

Automated detection of wedge-shaped defects in polarimetric images of the retinal nerve fibre layer

Purpose: Automated glaucoma detection in images obtained by scanning laser polarimetry is currently insensitive to local abnormalities, impairing its performance. The purpose of this investigation was to tes

Scanning Laser Polarimetry and Optical Coherence Tomography for Detection of Retinal Nerve Fiber Layer Defects

Purpose: To compare the ability of scanning laser polarimetry with variable corneal compensation (GDx-VCC) and Stratus optical coherence tomography (OCT) to detect photographic retinal nerve fiber layer (RNFL) defects. Methods: This retrospective cross-sectional study included 45 eyes of 45 consecutive glaucoma patients with RNFL defects in red-free fundus photographs. The superior and inferior temporal quadrants in each eye were included for data analysis separately. The location and presence of RNFL defects seen in red-free fundus photographs were compared with those seen in GDx-VCC deviation maps and OCT RNFL analysis maps for each quadrant. Results: Of the 90 quadrants (45 eyes), 31 (34%) had no apparent RNFL defects, 29 (32%) had focal RNFL defects, and 30 (33%) had diffuse RNFL defects in red-free fundus photographs. The highest agreement between GDx-VCC and red-free photography was 73 % when we defined GDx-VCC RNFL defects as a cluster of three or more color-coded squares (p<5%) along the traveling line of the retinal nerve fiber in the GDx-VCC deviation map (kappa value, 0.388; 95 % confidence interval (CI), 0.195 to 0.582). The highest agreement between OCT and red-free pho-tography was 85 % (kappa value, 0.666; 95 % CI, 0.506 to 0.825) when a value of 5 % outside the normal limit for the OCT analysis map was used as a cut-off value for OCT RNFL defects. Conclusions: According to the kappa values, the agreement between GDx-VCC deviation maps and red-free pho-tography was poor, whereas the agreement between OCT analysis maps and red-free photography was good

Deep-Layer Microvasculature Dropout by Optical Coherence Tomography Angiography and Microstructure of Parapapillary Atrophy.

Purpose:To investigate the association between the microstructure of β-zone parapapillary atrophy (βPPA) and parapapillary deep-layer microvasculature dropout assessed by optical coherence tomography angiography (OCT-A). Methods:Thirty-seven eyes with βPPA devoid of the Bruch's membrane (BM) (γPPA) ranging between completely absent and discontinuous BM were matched by severity of the visual field (VF) damage with 37 eyes with fully intact BM (βPPA+BM) based on the spectral-domain (SD) OCT imaging. Parapapillary deep-layer microvasculature dropout was defined as a dropout of the microvasculature within choroid or scleral flange in the βPPA on the OCT-A. The widths of βPPA, γPPA, and βPPA+BM were measured on six radial SD-OCT images. Prevalence of the dropout was compared between eyes with and without γPPA. Logistic regression was performed for evaluating association of the dropout with the width of βPPA, γPPA, and βPPA+BM, and the γPPA presence. Results:Eyes with γPPA had significantly higher prevalence of the dropout than did those without γPPA (75.7% versus 40.8%; P = 0.004). In logistic regression, presence and longer width of the γPPA, worse VF mean deviation, and presence of focal lamina cribrosa defects were significantly associated with the dropout (P < 0.05), whereas width of the βPPA and βPPA+BM, axial length, and choroidal thickness were not (P > 0.10). Conclusions:Parapapillary deep-layer microvasculature dropout was associated with the presence and larger width of γPPA, but not with the βPPA+BM width. Presence and width of the exposed scleral flange, rather than the retinal pigmented epithelium atrophy, may be associated with deep-layer microvasculature dropout