Optic nerve head three-dimensional shape analysis

We present a method for optic nerve head (ONH) 3-D shape analysis from retinal optical coherence tomography (OCT). The possibility to noninvasively acquire in vivo high-resolution 3-D volumes of the ONH using spectral domain OCT drives the need to develop tools that quantify the shape of this structure and extract information for clinical applications. The presented method automatically generates a 3-D ONH model and then allows the computation of several 3-D parameters describing the ONH. The method starts with a high-resolution OCT volume scan as input. From this scan, the model-defining inner limiting membrane (ILM) as inner surface and the retinal pigment epithelium as outer surface are segmented, and the Bruch's membrane opening (BMO) as the model origin is detected. Based on the generated ONH model by triangulated 3-D surface reconstruction, different parameters (areas, volumes, annular surface ring, minimum distances) of different ONH regions can then be computed. Additionally, the bending energy (roughness) in the BMO region on the ILM surface and 3-D BMO-MRW surface area are computed. We show that our method is reliable and robust across a large variety of ONH topologies (specific to this structure) and present a first clinical application

CuBe: parametric modeling of 3D foveal shape using cubic Bézier

Optical coherence tomography (OCT) allows three-dimensional (3D) imaging of the retina, and is commonly used for assessing pathological changes of fovea and macula in many diseases. Many neuroinflammatory conditions are known to cause modifications to the fovea shape. In this paper, we propose a method for parametric modeling of the foveal shape. Our method exploits invariant features of the macula from OCT data and applies a cubic Bézier polynomial along with a least square optimization to produce a best fit parametric model of the fovea. Additionally, we provide several parameters of the foveal shape based on the proposed 3D parametric modeling. Our quantitative and visual results show that the proposed model is not only able to reconstruct important features from the foveal shape, but also produces less error compared to the state-of-the-art methods. Finally, we apply the model in a comparison of healthy control eyes and eyes from patients with neuroinflammatory central nervous system disorders and optic neuritis, and show that several derived model parameters show significant differences between the two groups

Altered fovea in AQP4-IgG-seropositive neuromyelitis optica spectrum disorders

OBJECTIVE: To investigate disease-specific foveal shape changes in patients with neuromyelitis optica spectrum disorders (NMOSDs) using foveal morphometry. METHODS: This cross-sectional study included macular spectral domain optical coherence tomography scans of 52 eyes from 28 patients with aquaporin-4 immunoglobulin G (AQP4-IgG)-seropositive NMOSD, 116 eyes from 60 patients with MS, and 123 eyes from 62 healthy controls (HCs), retrospectively, and an independent confirmatory cohort comprised 33/33 patients with NMOSD/MS. The fovea was characterized using 3D foveal morphometry. We included peripapillary retinal nerve fiber layer (pRNFL) thickness and combined macular ganglion cell and inner plexiform layer (GCIPL) volume to account for optic neuritis (ON)-related neuroaxonal damage. RESULTS: Group comparison showed significant differences compared with HC in the majority of foveal shape parameters in NMOSD, but not MS. Pit flat disk area, average pit flat disk diameter, inner rim volume, and major slope disk length, as selected parameters, showed differences between NMOSD and MS (p value = 0.017, 0.002, 0.005, and 0.033, respectively). This effect was independent of ON. Area under the curve was between 0.7 and 0.8 (receiver operating characteristic curve) for discriminating between NMOSD and MS. Pit flat disk area and average pit flat disk diameter changes independent of ON were confirmed in an independent cohort. CONCLUSIONS: Foveal morphometry reveals a wider and flatter fovea in NMOSD in comparison to MS and HC. Comparison to MS and accounting for ON suggest this effect to be at least in part independent of ON. This supports a primary retinopathy in AQP4-IgG–seropositive NMOSD

Spinocerebellar ataxia type 14: refining clinicogenetic diagnosis in a rare adult-onset disorder

OBJECTIVES: Genetic variant classification is a challenge in rare adult-onset disorders as in SCA-PRKCG (prior spinocerebellar ataxia type 14) with mostly private conventional mutations and nonspecific phenotype. We here propose a refined approach for clinicogenetic diagnosis by including protein modeling and provide for confirmed SCA-PRKCG a comprehensive phenotype description from a German multi-center cohort, including standardized 3D MR imaging. METHODS: This cross-sectional study prospectively obtained neurological, neuropsychological, and brain imaging data in 33 PRKCG variant carriers. Protein modeling was added as a classification criterion in variants of uncertain significance (VUS). RESULTS: Our sample included 25 cases confirmed as SCA-PRKCG (14 variants, thereof seven novel variants) and eight carriers of variants assigned as VUS (four variants) or benign/likely benign (two variants). Phenotype in SCA-PRKCG included slowly progressive ataxia (onset at 4-50 years), preceded in some by early-onset nonprogressive symptoms. Ataxia was often combined with action myoclonus, dystonia, or mild cognitive-affective disturbance. Inspection of brain MRI revealed nonprogressive cerebellar atrophy. As a novel finding, a previously not described T2 hyperintense dentate nucleus was seen in all SCA-PRKCG cases but in none of the controls. INTERPRETATION: In this largest cohort to date, SCA-PRKCG was characterized as a slowly progressive cerebellar syndrome with some clinical and imaging features suggestive of a developmental disorder. The observed non-ataxia movement disorders and cognitive-affective disturbance may well be attributed to cerebellar pathology. Protein modeling emerged as a valuable diagnostic tool for variant classification and the newly described T2 hyperintense dentate sign could serve as a supportive diagnostic marker of SCA-PRKCG

Human Tau isoform-specific presynaptic deficits in a Drosophila Central Nervous System circuit

Accumulation of normal or mutant human Tau isoforms in Central Nervous System (CNS) neurons of vertebrate and invertebrate models underlies pathologies ranging from behavioral deficits to neurodegeneration that broadly recapitulate human Tauopathies. Although some functional differences have begun to emerge, it is still largely unclear whether normal and mutant Tau isoforms induce differential effects on the synaptic physiology of CNS neurons. We use the oligosynaptic Giant Fiber System in the adult Drosophila CNS to address this question and reveal that 3R and 4R isoforms affect distinct synaptic parameters. Whereas 0N3R increased failure rate upon high frequency stimulation, 0N4R compromised stimulus conduction and response speed at a specific cholinergic synapse in an age-dependent manner. In contrast, accumulation of the R406W mutant of 0N4R induced mild, age-dependent conduction velocity defects. Because 0N4R and its mutant isoform are expressed equivalently, this demonstrates that the defects are not merely consequent of exogenous human Tau accumulation and suggests distinct functional properties of 3R and 4R isoforms in cholinergic presynapses. © 201

Central macular topographic and volumetric measures: new biomarkers for detection of glaucoma

PURPOSE: To test the hypothesis that newly developed shape measures using optical coherence tomography (OCT) macular volume scans can discriminate patients with perimetric glaucoma from healthy subjects. METHODS: OCT structural measures defining macular topography and volume were recently developed based on cubic Bézier curves. We exported macular volume scans from 135 eyes with glaucoma (133 patients) and 155 healthy eyes (85 subjects) and estimated global and quadrant-based measures. The best subset of measures to predict glaucoma was explored with a gradient boost model (GBM) with subsequent logistic regression. Accuracy and area under receiver operating curves (AUC) were the primary metrics. In addition, we separately investigated model performance in 66 eyes with mild glaucoma (mean deviation ≥ -6 dB). RESULTS: Average (±SD) 24-2 mean deviation was -8.2 (±6.1) dB in eyes with glaucoma. The main predictive measures for glaucoma were temporal inferior rim height, nasal inferior pit volume, and temporal inferior pit depth. Lower values for these measures predicted higher risk of glaucoma. Sensitivity, specificity, and AUC for discriminating between healthy and glaucoma eyes were 81.5% (95% CI = 76.6-91.9%), 89.7% (95% CI = 78.7-94.2%), and 0.915 (95% CI = 0.882-0.948), respectively. Corresponding metrics for mild glaucoma were 84.8% (95% CI = 72.1%-95.5%), 85.8% (95% CI = 87.1%-97.4%), and 0.913 (95% CI = 0.867-0.958), respectively. CONCLUSIONS: Novel macular shape biomarkers detect early glaucoma with clinically relevant performance. Such biomarkers do not depend on intraretinal segmentation accuracy and may be helpful in eyes with suboptimal macular segmentation. TRANSLATIONAL RELEVANCE: Macular shape biomarkers provide valuable information for detection of early glaucoma and may provide additional information beyond thickness measurements

Automatic quality evaluation as assessment standard for optical coherence tomography

Retinal optical coherence tomography (OCT) is increasingly used for quantifying neuroaxonal damage in diseases of the central nervous system such as multiple sclerosis. High-quality OCT images are essential for accurate intraretinal segmentation and for correct quantification of retinal thickness changes. The quality of OCT images depends largely on the operator and patient compliance. Quality evaluation is time-consuming, and current OCT image quality criteria depend on the experience of the grader and are therefore subjective. The automatic graderindependent real-time feedback system for quality evaluation of retinal OCT images, AQuA, was developed to standardize quality evaluation and data accuracy. It classifies by signal quality, anatomical completeness and segmentation plausibility and has been validated by experienced graders. However, it is currently limited to OCT scans taken with one device from a single vendor. The aim of this work is to improve the capability of the AQuA quality classifier to generalize to new data, by developing a convolutional neural network (CNN), AQuANet. Moreover, this CNN may serve as a basic quality classifier, that can be adapted to specific problems by transfer learning. AQuANet is trained on A-Scan batches with quality labels automatically obtained with AQuA. Thus, a large set of training data of about 13000 A-Scan batches could be used, leading to an accuracy of 99.53%

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

Retinal optical coherence tomography (OCT) with intraretinal layer segmentation is increasingly used not only in ophthalmology but also for neurological diseases such as multiple sclerosis (MS). Signal quality influences segmentation results, and high-quality OCT images are needed for accurate segmentation and quantification of subtle intraretinal layer changes. Among others, OCT image quality depends on the ability to focus, patient compliance and operator skills. Current criteria for OCT quality define acceptable image quality, but depend on manual rating by experienced graders and are time consuming and subjective. In this paper, we propose and validate a standardized, grader-independent, real-time feedback system for automatic quality assessment of retinal OCT images. We defined image quality criteria for scan centering, signal quality and image completeness based on published quality criteria and typical artifacts identified by experienced graders when inspecting OCT images. We then trained modular neural networks on OCT data with manual quality grading to analyze image quality features. Quality analysis by a combination of these trained networks generates a comprehensive quality report containing quantitative results. We validated the approach against quality assessment according to the OSCAR-IB criteria by an experienced grader. Here, 100 OCT files with volume, circular and radial scans, centered on optic nerve head and macula, were analyzed and classified. A specificity of 0.96, a sensitivity of 0.97 and an accuracy of 0.97 as well as a Matthews correlation coefficient of 0.93 indicate a high rate of correct classification. Our method shows promising results in comparison to manual OCT grading and may be useful for real-time image quality analysis or analysis of large data sets, supporting standardized application of image quality criteria