Image database system for glaucoma diagnosis support

Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.

Tools for creating wide-field views of the human retina using Optical Coherence Tomography

Optical Coherence Tomography (OCT) has allowed in-vivo viewing of details of retinal layers like never before. With the development of spectral domain OCT (SD-OCT) details of nearly 2µm axial resolution and higher imaging speed have been reported. Nevertheless, a single volume scan of the retina is typically restricted to 6mm x 6mm in size. Having a larger field of view of the retina will definitely enhance the clinical utility of the OCT. A tool was developed for creating wide-field thickness maps of the retina by combining the use of already available tools like i2k Retina (DualAlign, LLC, Clifton Park, NY) and the thickness maps from Cirrus HD-OCT research browser (Carl Zeiss Meditec, Dublin, California, USA). Normal subjects (n=20) were imaged on Zeiss Cirrus HD-OCT using 512x128 Macular Cube scanning protocol. Sixteen overlapping volumetric images were obtained by moving the internal fixation target around such that the final stitched maps were 12mm x 14mm in size. The thickness maps were corrected for inter-individual differences in axial lengths measured using Zeiss IOL Master and averaged to obtain a normative map. An algorithm was also developed for montaging 3-D volume scans. Using this algorithm two OCT volume scans can be registered and stitched together to obtain a larger volume scan. The algorithm can be described as a two step process involving 3-D phase-correlation and 2-D Pseudo-polar Fourier transform (PPFT). In the first step, 3-D phase-correlation provides translation values in the x, y and z axis. The second step involves applying PPFT on each overlapping pair of B-scans to find rotation in the x-y plane. Subsequent volumes can be stitched to obtain a large field of view. We developed a simple and robust method for creating wide-field views of the retina using existing SD-OCT hardware. As segmentation algorithms improve, this method could be expanded to produce wide-field maps of retinal sub-layers, such as the outer nuclear layer or retinal nerve fiber layer. These wide-field views of the retina may prove useful in evaluating retinal diseases involving the peripheral retina (e.g., retinitis pigmentosa and glaucoma)

Improvement of the clinical utility of optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) measurement by establishing data comparability across the OCT technology generations and models

Glaucoma is the second leading cause of blindness worldwide, which induces irreversible structural damage (retinal ganglion cell loss and retinal nerve fiber layer (RNFL) thinning) on the retina. Optical coherence tomography (OCT) provides RNFL thickness measurements, which have become an essential clinical measure for objective glaucoma assessment. RNFL thickness is measured on a cross-sectional retinal image sampled along a 3.4mm circle centered around the optic nerve head (ONH). With the conventional time-domain OCT (TD-OCT), its operator dependent scan registration is responsible for the majority of measurement variability. Recently, spectral domain OCT (SD-OCT) technology has been introduced. SD-OCT provides faster scanning (up to 100x) and finer axial resolution (up to 2x) compared to TD-OCT, allowing three-dimensional (3D) volume sampling. 3D SD-OCT data can be visualized as an en face image of the retina. This allows us to create a virtual OCT image along any sampling line (curved or straight), which permits virtually perfect scan registration. The objective of this study is to improve the clinical utility of OCT RNFL measurement by establishing data comparability across the multiple OCT generations and models. First, we developed an algorithm to match the TD-OCT scan location within the corresponding 3D SD-OCT volume. Scan location matching (SLM) enables computation of the calibration equation between TD-OCT and SD-OCT for direct comparison of measurements, bridging the old technology with new ones. Second, the performance of the SLM method was measured using various SD-OCT devices with different spatial sampling methods. By making TD-OCT measurements at one time point comparable to the most recent SD-OCT measurement using SLM, glaucoma progression can be assessed on one to one basis. However, due to the variable TD-OCT scan registration over multiple visits, one can still not analyze the trend of glaucoma progression because RNFL thickness measured at different locations is not directly comparable even after calibration. Therefore, we developed a mathematical model of the retinal nerve fiber bundle distribution pattern to normalize the off-centered TD-OCT RNFL thickness to a virtually centered one. The outcome of this study would facilitate more accurate and reliable glaucoma disease/progression detection in cross-sectional as well as longitudinal clinical settings

Effects of Intraframe Distortion on Measures of Cone Mosaic Geometry from Adaptive Optics Scanning Light Ophthalmoscopy

Purpose: To characterize the effects of intraframe distortion due to involuntary eye motion on measures of cone mosaic geometry derived from adaptive optics scanning light ophthalmoscope (AOSLO) images. Methods: We acquired AOSLO image sequences from 20 subjects at 1.0, 2.0, and 5.08 temporal from fixation. An expert grader manually selected 10 minimally distorted reference frames from each 150-frame sequence for subsequent registration. Cone mosaic geometry was measured in all registered images (n ¼ 600) using multiple metrics, and the repeatability of these metrics was used to assess the impact of the distortions from each reference frame. In nine additional subjects, we compared AOSLO-derived measurements to those from adaptive optics (AO)-fundus images, which do not contain system-imposed intraframe distortions. Results: We observed substantial variation across subjects in the repeatability of density (1.2%–8.7%), inter-cell distance (0.8%–4.6%), percentage of six-sided Voronoi cells (0.8%–10.6%), and Voronoi cell area regularity (VCAR) (1.2%–13.2%). The average of all metrics extracted from AOSLO images (with the exception of VCAR) was not significantly different than those derived from AO-fundus images, though there was variability between individual images. Conclusions: Our data demonstrate that the intraframe distortion found in AOSLO images can affect the accuracy and repeatability of cone mosaic metrics. It may be possible to use multiple images from the same retinal area to approximate a ‘‘distortionless’’ image, though more work is needed to evaluate the feasibility of this approach. Translational Relevance: Even in subjects with good fixation, images from AOSLOs contain intraframe distortions due to eye motion during scanning. The existence of these artifacts emphasizes the need for caution when interpreting results derived from scanning instruments

Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography

Purpose. Microaneurysms (MAs) are considered a hallmark of retinal vascular disease, yet what little is known about them is mostly based upon histology, not clinical observation. Here, we use the recently developed adaptive optics scanning light ophthalmoscope (AOSLO) fluorescein angiography (FA) to image human MAs in vivo and to expand on previously described MA morphologic classification schemes. Methods. Patients with vascular retinopathies (diabetic, hypertensive, and branch and central retinal vein occlusion) were imaged with reflectance AOSLO and AOSLO FA. Ninety-three MAs, from 14 eyes, were imaged and classified according to appearance into six morphologic groups: focal bulge, saccular, fusiform, mixed, pedunculated, and irregular. The MA perimeter, area, and feret maximum and minimum were correlated to morphology and retinal pathology. Select MAs were imaged longitudinally in two eyes. Results. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging revealed microscopic features of MAs not appreciated on conventional images. Saccular MAs were most prevalent (47%). No association was found between the type of retinal pathology and MA morphology (P = 0.44). Pedunculated and irregular MAs were among the largest MAs with average areas of 4188 and 4116 μm2, respectively. Focal hypofluorescent regions were noted in 30% of MAs and were more likely to be associated with larger MAs (3086 vs. 1448 μm2, P = 0.0001). Conclusions. Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response

Motion Correction in Optical Coherence Tomography for Multi-modality Retinal Image Registration

Optical coherence tomography (OCT) is a recently developed non-invasive imaging modality, which is often used in ophthalmology. Because of the sequential scanning in form of A-scans, OCT suffers from the inevitable eye movement. This often leads to mis-alignment especially among consecutive B-scans, which affects the analysis and processing of the data such as the registration of the OCT en face image to color fundus image. In this paper, we propose a novel method to correct the mis-alignment among consecutive B-scans to improve the accuracy in multi-modality retinal image registration. In the method, we propose to compute decorrelation from overlapping B-scans and to detect the eye movement. Then, the B-scans with eye movement will be re-aligned to its precedent scans while the rest of B-scans without eye movement are untouched. Our experiments results show that the proposed method improves the accuracy and success rate in the registration to color fundus images

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases