Methods to extract and quantify retinal blood vessels and optic nerve head from optical coherence tomography data in neurological disorders

Abstract

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 Selbstandigkeitserklarung Zusammenfassung