Active contour method for ILM segmentation in ONH volume scans in retinal OCT

The optic nerve head (ONH) is affected by many neurodegenerative and autoimmune inflammatory conditions. Optical coherence tomography can acquire high-resolution 3D ONH scans. However, the ONH's complex anatomy and pathology make image segmentation challenging. This paper proposes a robust approach to segment the inner limiting membrane (ILM) in ONH volume scans based on an active contour method of Chan-Vese type, which can work in challenging topological structures. A local intensity fitting energy is added in order to handle very inhomogeneous image intensities. A suitable boundary potential is introduced to avoid structures belonging to outer retinal layers being detected as part of the segmentation. The average intensities in the inner and outer region are then resealed locally to account for different brightness values occurring among the ONH center. The appropriate values for the parameters used in the complex computational model are found using an optimization based on the differential evolution algorithm. The evaluation of results showed that the proposed framework significantly improved segmentation results compared to the commercial solution

Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline

Neurodegenerative and neuroinflammatory diseases regularly cause optic nerve and retinal damage. Evaluating retinal changes using optical coherence tomography (OCT) in diseases like multiple sclerosis has thus become increasingly relevant. However, intraretinal segmentation, a necessary step for interpreting retinal changes in the context of these diseases, is not standardized and often requires manual correction. Here we present a semi-automatic intraretinal layer segmentation pipeline and establish normative values for retinal layer thicknesses at the macula, including dependencies on age, sex, and refractive error. Spectral domain OCT macular 3D volume scans were obtained from healthy participants using a Heidelberg Engineering Spectralis OCT. A semi-automated segmentation tool (SAMIRIX) based on an interchangeable third-party segmentation algorithm was developed and employed for segmentation, correction, and thickness computation of intraretinal layers. Normative data is reported froma 6mmEarly Treatment Diabetic Retinopathy Study (ETDRS) circle around the fovea. An interactive toolbox for the normative database allows surveying for additional normative data. We cross-sectionally evaluated data from218 healthy volunteers (144 females/74males, age 36.5 ± 12.3 years, range 18–69 years). Average macular thickness (MT) was 313.70 ± 12.02 μm, macular retinal nerve fiber layer thickness (mRNFL) 39.53 ± 3.57 μm, ganglion cell and inner plexiform layer thickness (GCIPL) 70.81 ± 4.87 μm, and inner nuclear layer thickness (INL) 35.93 ± 2.34 μm. All retinal layer thicknesses decreased with age. MT and GCIPL were associated with sex, with males showing higher thicknesses. Layer thicknesses were also positively associated with each other. Repeated-measurement reliability for the manual correction of automatic intraretinal segmentation results was excellent, with an intra-class correlation coefficient >0.99 for all layers. The SAMIRIX toolbox can simplify intraretinal segmentation in research applications, and the normative data application may serve as an expandable reference for studies, in which normative data cannot be otherwise obtained

Comparison of Standard Versus Wide-Field Composite Images of the Corneal Subbasal Layer by In Vivo Confocal Microscopy

PURPOSE. To evaluate whether the densities of corneal subbasal nerves and epithelial immune dendritiform cells (DCs) are comparable between a set of three representative standard images of in vivo confocal microscopy (IVCM) and the wide-field mapped composite IVCM images. METHODS. This prospective, cross-sectional, and masked study included 110 eyes of 58 patients seen in a neurology clinic who underwent laser-scanning IVCM (Heidelberg Retina Tomograph 3) of the central cornea. Densities of subbasal corneal nerves and DCs were compared between the average of three representative standard images and the wide-field mapped composite images, which were reconstructed by automated mapping. RESULTS. There were no statistically significant differences between the average of three representative standard images (0.16 mm 2 each) and the wide-field composite images (1.29 6 0.64 mm 2 ) in terms of mean subbasal nerve density (17.10 6 6.10 vs. 17.17 6 5.60 mm/mm 2 , respectively, P ¼ 0.87) and mean subbasal DC density (53.2 6 67.8 vs. 49.0 6 54.3 cells/mm 2 , respectively, P ¼ 0.43). However, there were notable differences in subbasal nerve and DC densities between these two methods in eyes with very low nerve density or very high DC density. CONCLUSIONS. There are no significant differences in the mean subbasal nerve and DC densities between the average values of three representative standard IVCM images and wide-field mapped composite images. Therefore, these standard images can be used in clinical studies to accurately measure cellular structures in the subbasal layer

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

Optic Nerve Head Quantification in Idiopathic Intracranial Hypertension by Spectral Domain OCT

Objective: To evaluate 3D spectral domain optical coherence tomography (SDOCT) volume scans as a tool for quantification of optic nerve head (ONH) volume as a potential marker for treatment effectiveness and disease progression in idiopathic intracranial hypertension (IIH). Design and Patients: Cross-sectional pilot trial comparing 19 IIH patients and controls matched for gender, age and body mass index. Each participant underwent SDOCT. A custom segmentation algorithm was developed to quantify ONH volume (ONHV) and height (ONHH) in 3D volume scans. Results:Whereas peripapillary retinal nerve fiber layer thickness did not show differences between controls and IIH patients, the newly developed 3D parameters ONHV and ONHH were able to discriminate between controls, treated and untreated patients. Both ONHV and ONHH measures were related to levels of intracranial pressure (ICP). Conclusion: Our findings suggest 3D ONH measures as assessed by SDOCT as potential diagnostic and progression markers in IIH and other disorders with increased ICP. SDOCT may promise a fast and easy diagnostic alternative to repeated lumba

Methoden zur Extrahierung und Quanitifzierung von retinalen Blutgefäßen und des Sehnervenkopfes aus Volumenscans der Optischen Kohärenztomographie bei neurologischen Erkrankungen

Contents Abstract Acknowledgements 1 Introduction 1.1 Motivation 1.2 Summary of main achievements 1.3 Publications 1.4 Overview of the thesis 2 Background 2.1 Computational and mathematical approaches for retinal feature extraction 2.1.1 Basics and Notations 2.1.2 Scale-space representation of image data 2.1.3 Coherence enhancing diffusion 2.1.4 Hessian based vesselness for vessel segmentation 2.1.5 Optimally oriented flux as a descriptor for tubular structures 2.1.6 Thin plate spline 2.2 Anterior visual system 2.2.1 Visual pathway 2.2.2 Retina anatomy and structures 2.2.3 Retinal blood supply 2.2.4 Optic nerve head 2.3 Retinal imaging techniques 2.3.1 Fundus photography 2.3.2 Stereo fundus photography 2.3.3 Confocal scanning laser ophthalmoscopy 2.3.4 Heidelberg retina tomograph 2.3.5 Optical coherence tomography. 2.4 Retina in neurological disorders and OCT parameters 2.4.1 Retina in MS 2.4.2 Retina in NMOSD 2.4.3 Retina in IIH 2.5 Data and optical coherence tomography device used in our research 3 Retinal blood vessel segmentation 3.1 Previous approaches in retinal blood vessel segmentation 3.2 Semi-automated tool for detection of blood vessel inner and outer diameter in cSLOimages 3.2.1 Double- Gaussian profile analysis 3.2.2 Validation 3.2.3 Results of a clinical study 3.3 Automated detection of the entire retinal vasculature in cSLO images 3.3.1 Approach 1. Extended 2D Morlet filtering with principal curvature enhancement 3.3.2 Approach 2. Improved vesselness response at vessel crossings 3.3.3 Approach3. New vesselness response based on OOF 3.3.4 Experimental results 4 RPE lower boundary segmentation for ONH volume computation 4.1 Previous approaches in RPE lower boundary segmentation 4.2 Algorithm description 4.2.1 RPE Region 4.2.2 RPE Initial Pixels 4.2.3 RPE Curve 4.3 Validation 4.4 Results of two clinical studies 5 BMO points detection for ONH center and ONH volume computation 5.1 Previous approaches in ONH volume computation 5.2 Algorithm description 5.2.1 Detection of ILM, ONL, and RPE lower boundary 5.2.2 Modified TPS fitting 5.2.3 Volume reduction 5.2.4 Vessel suppression 5.2.5 BMO points detection using textural information in a grow-cut setting 5.3 Validation 5.4 Results of a clinical study 6 Discussion 6.1 Semi-automated tool for detection of blood vessel inner and outer diameter in cSLOimages 6.2 Detection of the entire retinal vasculature in cSLO images 6.3 RPE lower boundary segmentation for ONH volume computation 6.4 BMO points detection for ONH center and ONH volume computation 7 Conclusion and Outlook Bibliography Selbständigkeitserklärung ZusammenfassungThe eye’s retina is considered to be part of the central nervous system with similar structure and cellular composition like the brain. Thus, it has gained an important role in identifying structural changes that provide useful diagnostic information in many neurological disorders. Over the last decade, innovative advances in optical imaging technology have allowed us to identify these changes in the retinal architecture. Especially optical coherence tomography (OCT) has become a powerful imaging modality in ophthalmology and vision science. OCT non-invasively acquires in micrometer-resolution, three- dimensional (3D), cross-sectional images of biological tissues in vivo, producing in-depth views of the retina. With the 3D data sets, we can use 3D modeling and detection tools to allow more intuitive visualization and quantification of the structure in the data set, similar to the 3D tools created for magnetic resonance imaging or computed tomographic scans. However, current OCT technology being mainly applied in the analysis and quantification of ophthalmological diseases lacks tailored image analysis methods for many changes caused by neurological disorders. The focus of this thesis lies on the development of segmentation and analysis methods to quantify two major components of the retina in confocal scanning laser ophthalmoscopy (cSLO data - 2D image) and in OCT data (3D OCT volume data), the retinal blood vessels, and the optic nerve head (ONH). The difficulty in developing robust and accurate methods for detecting these structures consists in the heterogeneous aspect of the data, coming from the natural anatomical diversity of the subjects, artifacts during data acquisition, especially in patients rather than in data from healthy control, and most importantly from certain structural changes that occur in the data during the disease course. We present four approaches for extracting features from the retinal vasculature and for the ONH in multiple sclerosis (with its subtypes), neuromyelitis optica spectrum disorder and idiopathic intracranial hypertension. The first two approaches focus on the detection of the vasculature in SLO images. We propose a new 2D model of the vessel profile that accounts for the central reflex seen in this particular image type in order to quantify the vessel inner and outer boundary. Furthermore, we developed new filter response measures for vessel enhancement based on Morlet wavelet, the Hessian tensor, and an optimal oriented flux approach, and tested their capability of correctly detecting the vessel inner and outer boundary, curvature especially in junction regions. In the case of the ONH, we present a robust approach to detect a reference surface for the volume computation in atrophic and swelled ONH. Moreover, we present a novel algorithm for the detection of the ONH center directly in the 3D OCT volume. The basic idea of this method is to use the information from the computed reference surface to reduce the computation to a sub-volume (a reduced volume) in the ONH region. Furthermore, we address several challenges present in our data: motion artifacts due to eye/head movements by using a modified thin plate spline fitting that is able to model the natural curvature of the retina, artifacts arising from the shadows created by the presence of blood vessel by incorporating contextual textural features in a 3D grow-cut setting. We evaluate our methods in various clinical settings. To demonstrate the effectiveness of our novel methods, we applied them to various patient and healthy control datasets.In der Retina, die dem zentralen Nervensystem zugeordnet wird, finden sich viele Zellarten und Strukturen, die auch im Gehirn vorkommen. Daher spielt die Erkennung struktureller Veränderungen der Retina eine wichtige Rolle in der Diagnose vieler neurologischer Erkrankungen. In den letzten Jahren haben innovative optische Verfahren die Bildgebung am Auge optimiert und ermöglichen die Erkennung solcher retinaler Veränderungen. Besonders die optische Kohärenztomographie hat sich als nützliches Bildgebungswerkzeug vor allem in der Augenheilkunde etabliert. OCT ist ein nicht-invasives Verfahren, welches in-vivo Aufnahmen von biologischem Gewebe und damit dreidimensionale (3D) Tiefenscans der Retina ermöglicht. Auf einen solchen 3D OCT Scan können 3D Modellierung und Detektionsmechanismen anwendet werden, um eine für den Anwender intuitivere Visualisierung und Quantifizierung der Strukturen zu erstellen, ähnlich wie 3D Verfahren für die Auswertung von Magnetresonanztomographie oder Computertomographieaufnahmen. Derzeit wird OCT jedoch vor allem in der Diagnose und Quantifizierung ophthalmologischer Erkrankungen der Netzhaut genutzt, die Geräte bieten nur begrenzte Analyseverfahren, die sich für die Beurteilung der Veränderungen durch neurologische Erkrankungen eignen. Daher liegt der Fokus dieser Arbeit in der Entwicklung von neuen Segmentations und Analyse- verfahren für die Quantifizierung zweier Bestandteile der Retina: Die retinalen Blutgefäße auf zweidimensionalen Konfokalen Scanning Laser Ophthalmoskopaufnahmen (cSLO), und den Sehnervenkopf (Optic nerve head, ONH) aus 3D OCT Volumenaufnahmen. Die Schwierigkeit in der Entwicklung robuster und akkurater Methoden für die Erkennung dieser Strukturen liegt in der Heterogenität der Daten, welche durch die natürliche anatomische Vielfalt, Artefakte während der Aufnahme, besonders bei Patienten im Vergleich zu gesunden Kontrollen, und vor allem wegen bestimmter struktureller Veränderungen im Krankheitsverlauf entsteht. Wir präsentieren vier Ansätze für die Extrahierung von Eigenschaften der retinalen Vaskularisierung und des ONH in Multipler Sklerose, Neuromyelitis Optica Spektrum-erkrankungen und idiopatisch erhöhtem Hirndruck. Die ersten beiden Ansätze konzentrieren sich auf die Erkennung der Blutgefäße im SLO Bild. Wir stellen ein neues 2D Model des Gefäßprofils vor, welches den auf diesen Aufnahmen sichtbaren Zentralreflex der Gefäße miteinbezieht, um so den inneren und äußeren Gefäßdurchmesser zu quantifizieren. Darüber hinaus haben wir neue Filter für die Hervorhebung der Blutgefäße, basierend auf Morlet-Wavelet, dem Hesse-Tensor und einem gerichteter Fluss-Ansatz, entwickelt und ihre Eignung für die korrekte Erkennung von inneren und äußeren Gefäßrändern und Krümmung der Blutgefäße, auch in Verzweigungen, geprüft. Für den ONH präsentieren wir einen robusten Ansatz für die Berechnung einer Referenz-oberfläche zur Volumenberechnung bei Schwellung und Atrophie. Zudem präsentieren wir einen neuen Algorithmus für die Erkennung des ONH Zentrums direkt im 3D Volumen. Die Grundidee der Methode ist die Nutzung von Informationen, die aus der Referenzoberfläche gewonnen wurden, um die Berechnung auf ein Sub-volumen um den ONH zu reduzieren. Darüber hinaus konnten wir mehrere Artefakte, die in unseren Daten zu finden waren, korrigieren: Bewegungsartefakte wegen Augen- und/oder Kopfbewegungen durch Nutzung eines modifizierten Thin Plate Spline Fittings, welches in der Lage ist die natürliche Krümmung der Retina zu modellieren, und durch Blutgefäße entstandene Schattenartefakte durch Texturanalyse mit einem Grow-cut Algorithmus. Um die Effektivität unserer neuen Methoden zu zeigen, wurden sie in Studien mit verschiedenen Patientengruppen sowie gesunden Kontrollen angewendet

Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets

Reliable biomarkers quantifying neurodegeneration and neuroinflammation in central nervous system disorders such as Multiple Sclerosis, Alzheimer’s dementia or Parkinson’s disease are an unmet clinical need. Intraretinal layer thicknesses on macular optical coherence tomography (OCT) images are promising noninvasive biomarkers querying neuroretinal structures with near cellular resolution. However, changes are typically subtle, while tissue gradients can be weak, making intraretinal segmentation a challenging task. A robust and efficient method that requires no or minimal manual correction is an unmet need to foster reliable and reproducible research as well as clinical application. Here, we propose and validate a cascaded two-stage network for intraretinal layer segmentation, with both networks being compressed versions of U-Net (CCU-INSEG). The first network is responsible for retinal tissue segmentation from OCT B-scans. The second network segments eight intraretinal layers with high fidelity. At the post-processing stage, we introduce Laplacian-based outlier detection with layer surface hole filling by adaptive non-linear interpolation. Additionally, we propose a weighted version of focal loss to minimize the foreground–background pixel imbalance in the training data. We train our method using 17,458 B-scans from patients with autoimmune optic neuropathies, i.e., multiple sclerosis, and healthy controls. Voxel-wise comparison against manual segmentation produces a mean absolute error of 2.3 μm, outperforming current state-of-the-art methods on the same data set. Voxel-wise comparison against external glaucoma data leads to a mean absolute error of 2.6 μm when using the same gold standard segmentation approach, and 3.7 μm mean absolute error in an externally segmented data set. In scans from patients with severe optic atrophy, 3.5% of B-scan segmentation results were rejected by an experienced grader, whereas this was the case in 41.4% of B-scans segmented with a graph-based reference method. The validation results suggest that the proposed method can robustly segment macular scans from eyes with even severe neuroretinal changes

ONH volume and height differences between IIH patients and controls. A

<p>) 3D spectral domain OCT ONH measurement from a matched control ONH <b>B</b>) 3D spectral domain OCT ONH measurement from an IIH patient with a diagnosed papilledema <b>C</b>) Groups differences in optic nerve head volume (ONHV) between IIH patients (black bar) and controls (white bar). <b>D</b>) Group difference in ONHV between medically untreated (gray bar) and treated (vertical lines bar) IIH patients. Error bars represent 1x standard deviation in figures c and d.  =  p <0.001.</p

Comparison of optical coherence tomography measurements between IIH patients and controls.

<p><b>Abbreviations</b>: RNFLT  =  retinal nerve fiber layer thickness; TMV  =  total macular volume; ONHV  =  optic nerve head volume; ONHH  =  optic nerve head height; SD  =  standard deviation; Min  =  minimum value; Max  =  maximum value; GEE  =  generalized estimating equation models analyses accounting for inter–eye/intra-patient dependencies; B  =  regression coefficient; SE  =  coefficient standard error; p  =  p value.</p