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

An approach to the quantitative assessment of retinal layer distortions and subretinal fluid in SD-OCT images

A modern tool for age-related macular degeneration (AMD) investigation is Optical Coherence Tomography (OCT), which can produce high resolution cross-sectional images of retinal layers. AMD is one of the most frequent reasons for blindness in economically developed countries. AMD means degeneration of the macula, which is responsible for central vision. Since AMD affects only this specific part of the retina, untreated patients lose their fine shape- and face recognition, reading ability, and central vision. Here, we deal with the automatic localization of subretinal fluid areas and also analyze retinal layers, since layer information can help to localize fluid regions. We present an algorithm that automatically delineates the two extremal retinal layers, successfully localizes subretinal fluid regions, and computes their extent. We present our results using a set of SD-OCT images. The quantitative information can also be visualized in an anatomical context for visual assessment

Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10278-020-00360-y[Abstract]: The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies and consequently the life quality of the patients. In this context, this paper proposes a fully automatic system for the identification, segmentation and characterization of the three ME types using optical coherence tomography (OCT) images. In the case of SRD and CME edemas, different approaches were implemented adapting graph cuts and active contours for their identification and precise delimitation. In the case of the DRT edemas, given their fuzzy regional appearance that requires a complex extraction process, an exhaustive analysis using a learning strategy was designed, exploiting intensity, texture, and clinical-based information. The different steps of this methodology were validated with a heterogeneous set of 262 OCT images, using the manual labeling provided by an expert clinician. In general terms, the system provided satisfactory results, reaching Dice coefficient scores of 0.8768, 0.7475, and 0.8913 for the segmentation of SRD, CME, and DRT edemas, respectively.This work is supported by the Instituto de Salud Carlos III, Government of Spain, and FEDER funds through the DTS18/00136 research project and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro de Investigación del Sistema Universitário de Galicia, Ref. ED431G 2019/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2016-04

Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

[EN] Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.This work was partially funded by Spanish National projects AES2017-PI17/00771 and AES2017-PI17/00821 (Instituto de Salud Carlos III), PID2019-105142RB-C21 (AI4SKIN) (Spanish Ministry of Economy and Competitiveness), PTA2017-14610-I (State Research Spanish Agency), regional project 20901/PI/18 (Fundacion Seneca) and Polytechnic University of Valencia (PAID-01-20).Berenguer-Vidal, R.; Verdú-Monedero, R.; Morales-Sánchez, J.; Sellés-Navarro, I.; Del Amor, R.; García-Pardo, JG.; Naranjo Ornedo, V. (2021). Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging. Sensors. 21(23):1-30. https://doi.org/10.3390/s21238027S130212

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. An early diagnosis is essential in order to prevent blindness. Ophthalmologists measure the deterioration caused by this disease by assessing the retinal layers in different regions of the eye, using different optical coherence tomography (OCT) scanning patterns to extract images, generating different views from multiple parts of the retina. These images are used to measure the thickness of the retinal layers in different regions. Methods: We present two approaches for the multi-region segmentation of the retinal layers in OCT images of glaucoma patients. These approaches can extract the relevant anatomical structures for glaucoma assessment from three different OCT scan patterns: circumpapillary circle scans, macular cube scans and optic disc (OD) radial scans. By employing transfer learning to take advantage of the visual patterns present in a related domain, these approaches use state-of-the-art segmentation modules to achieve a robust, fully automatic segmentation of the retinal layers. The first approach exploits inter-view similarities by using a single module to segment all of the scan patterns, considering them as a single domain. The second approach uses view-specific modules for the segmentation of each scan pattern, automatically detecting the suitable module to analyse each image. Results: The proposed approaches produced satisfactory results with the first approach achieving a dice coefficient of 0.85±0.06 and the second one 0.87±0.08 for all segmented layers. The first approach produced the best results for the radial scans. Concurrently, the view-specific second approach achieved the best results for the better represented circle and cube scan patterns. Conclusions: To the extent of our knowledge, this is the first proposal in the literature for the multi-view segmentation of the retinal layers of glaucoma patients, demonstrating the applicability of machine learningbased systems for aiding in the diagnosis of this relevant pathology

Retina layer segmentation using kernel graph cuts and continuous max-flow

Circular scan Spectral-Domain Optic Coherence Tomography imaging (SD-OCT) is one of the best tools for diagnosis of retinal diseases. This technique provides more comprehensive detail of the retinal morphology and layers around the optic disc nerve head (ONH). Since manual labelling of the retinal layers can be tedious and time consuming, accurate and robust automated segmentation methods are needed to provide the thickness evaluation of these layers in retinal disorder assessments such as glaucoma. The proposed method serves this purpose by performing the segmentation of retinal layers boundaries in circular SD-OCT scans acquired around the ONH. The layers are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. Results from scan images acquired with Spectralis (Heidelberg Engineering, Germany) prove that the proposed method is robust and efficient in detecting the retinal layers boundaries in images. With a mean root-mean-square error (RMSE) of 0.0835 ± 0.0495 and an average Dice coefficient of 0.9468 ± 0.0705 pixels for the retinal nerve fibre layer thickness, the proposed method demonstrated effective agreement with manual annotations

Neue Methoden der Nachbearbeitung und Analyse retinaler optischer Kohärenztomografieaufnahmen bei neurologischen Erkrankungen

Viele neurologische Krankheiten verursachen Veränderungen in der Netzhaut, die mit Hilfe der optischen Kohärenztomography (optical coherence tomography, OCT) dargestellt werden können. Dabei entstehen viele Bilddaten, deren Auswertung zeitintensiv ist und geschultes Personal erfordert. Ziel dieser Arbeit war die Entwicklung neuer Methoden zur Vorverarbeitung und Analyse retinaler OCT-Bilddaten, um Outcome-Parameter für Studien und diagnostische Marker für neurologische Erkrankungen zu verbessern. Dazu wurden Methoden für zwei wichtige Aufnahmebereiche der Netzhaut, den Sehnervenkopf (optic nerve head, ONH) und die Makula, entwickelt. Für den ONH-Bereich wurde eine automatische Segmentierung auf Basis aktiver Konturen entwickelt, die eine akkurate Segmentierung der inneren Grenzmembran auch bei komplexer Topografie ermöglicht. Für den Bereich um die Makula entstand eine intraretinale Schichtensegmentierungspipeline, die von der Auswahl der Bilddaten über die automatische Segmentierung sowie die manuelle Nachkorrektur bis zur Ausgabe verschiedener Schichtdicken in Tabellenform reicht. Für beide Aufnahmebereiche wurden mehrere Programme entwickelt, die auf einer gemeinsamen Basis zur Verarbeitung von OCT-Daten fußen. Eines dieser Programme bietet eine grafische Oberfläche zur manuellen Verarbeitung der Bilddaten. Mit dieser Software wurden Teile der Referenzdaten manuell erstellt, die innere Grenzmembran des ONH automatisch segmentiert sowie eine komfortable Nachbearbeitung von intraretinalen Segmentierungen vorgenommen. Dies ermöglichte die automatische Auswertung morphologischer Parameter des ONH, wovon einige signifikante Unterschiede zwischen Patienten mit neurologischen Krankheiten und gesunden Kontrollen zeigten. Weiter kam die Schichtensegmentierungspipeline beim Aufbau einer normativen Datenbank sowie in einer Studie zum Zusammenhang des retinalen Schadens mit der kritischen Flimmerfrequenz zum Einsatz. Ein Teil der Software wurde als freie und quelloffene Software (free and open-source software, FOSS) und der normative Datensatz für die Verwendung in anderen Studien freigegeben. Beides wird bereits in weiteren Studien eingesetzt und wird auch die Durchführung zukünftiger Studien vereinfachen sowie die Entwicklung neuer Methoden unterstützen.Many neurological diseases cause changes in the retina, which can be visualized using optical coherence tomography (OCT). This process produces large amounts of image data. Its evaluation is time-consuming and requires medically trained personnel. This dissertation aims to develop new methods for preprocessing and analyzing retinal OCT data in order to improve outcome parameters for clinical studies and diagnostic markers for neurological diseases. For this purpose, methods concerning the regions of two landmarks of the retina, the optic nerve head (ONH) and the macula, were developed. For the ONH, an automatic segmentation method based on active contours was developed, which allows accurate segmentation of the inner limiting membrane even in complex topography. For the macular region, an intraretinal layer segmentation pipeline from image data via automatic segmentation to manual post-correction and the output of different layer thicknesses in tabular form was developed. For both, ONH and macular region, several programs were developed, which share a common basis for processing OCT data. One of these programs offers a graphical user interface for the manual processing of image data. Parts of the reference data were created manually using this software. Moreover, the inner limiting membrane of the ONH was segmented automatically and post-processing of intraretinal segmentations was performed. This allowed for automatic evaluation of morphological parameters of the ONH, some of which showed significant differences between patients with neurological diseases and the healthy control group. Furthermore, the layer segmentation pipeline was utilized to create a normative database as well as to investigate the correlation of retinal damage and critical flicker frequency. Part of the software was released as free and open-source software (FOSS) and the normative data set was released for use in other studies. Both are already being used in further studies and will also aid in future studies, as well as support the development of new methods