Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with the diagnosis of several ophthalmic diseases. For example, diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Despite contemporary advances, automatic segmentation of the choroid layer remains a challenging task due to low contrast, inhomogeneous intensity, inconsistent texture and ambiguous boundaries between the choroid and sclera in Optical Coherence Tomography (OCT) images. The majority of currently implemented methods manually or semi-automatically segment out the region of interest. While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector. This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations. The aim of this research was to segment out Bruch’s Membrane (BM) and choroid layer to calculate the thickness map. BM was segmented using a series of morphological operations, whereas the choroid layer was segmented using a deep learning approach as more image statistics were required to segment accurately. Several evaluation metrics were used to test and compare the proposed method against other existing methodologies. Experimental results showed that the proposed method greatly reduced the error rate when compared with the other state-of-the art methods

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

Level set segmentation of retinal structures

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Changes in retinal structure are related to different eye diseases. Various retinal imaging techniques, such as fundus imaging and optical coherence tomography (OCT) imaging modalities, have been developed for non-intrusive ophthalmology diagnoses according to the vasculature changes. However, it is time consuming or even impossible for ophthalmologists to manually label all the retinal structures from fundus images and OCT images. Therefore, computer aided diagnosis system for retinal imaging plays an important role in the assessment of ophthalmologic diseases and cardiovascular disorders. The aim of this PhD thesis is to develop segmentation methods to extract clinically useful information from these retinal images, which are acquired from different imaging modalities. In other words, we built the segmentation methods to extract important structures from both 2D fundus images and 3D OCT images. In the first part of my PhD project, two novel level set based methods were proposed for detecting the blood vessels and optic discs from fundus images. The first one integrates Chan-Vese's energy minimizing active contour method with the edge constraint term and Gaussian Mixture Model based term for blood vessels segmentation, while the second method combines the edge constraint term, the distance regularisation term and the shape-prior term for locating the optic disc. Both methods include the pre-processing stage, used for removing noise and enhancing the contrast between the object and the background. Three automated layer segmentation methods were built for segmenting intra-retinal layers from 3D OCT macular and optic nerve head images in the second part of my PhD project. The first two methods combine different methods according to the data characteristics. First, eight boundaries of the intra-retinal layers were detected from the 3D OCT macular images and the thickness maps of the seven layers were produced. Second, four boundaries of the intra-retinal layers were located from 3D optic nerve head images and the thickness maps of the Retinal Nerve Fiber Layer (RNFL) were plotted. Finally, the choroidal layer segmentation method based on the Level Set framework was designed, which embedded with the distance regularisation term, edge constraint term and Markov Random Field modelled region term. The thickness map of the choroidal layer was calculated and shown.Department of Computer Science, Brunel University London

Evaluation of the potentials for optical coherence tomography (OCT) to detect early signs of retinal neurodegeneration

Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA). Prior to the application of SVM and PCA for the classification of human OCT images, evaluation of the classifiers was performed in the classification of optical phantoms, the accuracy of which was in the range of 82-100%. This was the first attempt to measure the textural properties of various polystyrene and silica beads optical phantoms. To identify optical changes that characterise early apoptosis, OCT imaging of axotomised retinal ganglion cells (RGCs) in ex vivo retinal murine explants was performed. Substantial optical alterations in RGC dendrites in the early stages of apoptosis (up to 2 hours) were detected. ML algorithms correctly classified the retinal texture of the inner plexiform layer (IPL) of transgenic AD mice in all cases, indicating the potential for further investigation in in vivo animal and human studies. Not only the optical signature but also the transparency of the dissected murine retinal explants was investigated. Moreover, ML classification of 3xTg mice IPL layer was studied in terms of optical changes due to the RGD dendritic atrophy. ML classifiers’ accuracy in the detection of early and neovascular AMD was 93-100% for the texture of retinal pigment epithelium, 69-67% for the outer nuclear layer, 70% for the inner segment and 60-90% for the outer segment of photoreceptors. Classification of AMD stages and comparison with the age-matched healthy controls was carried out in the outer retina and RPE. Grey-level co-occurrence, run-length matrices, local binary patterns features were extracted from the IPL of the macula to classify glaucoma OCT images. The accuracy of linear and non-linear SVMs, linear and quadratic discriminant analyses, decision tree and logistic regression was between 55-70%. Based on the classifiers’ precision, recall and F1-score, Gaussian SVM outperformed other ML techniques. In this study, the observation of early glaucomatous subtle optical changes of human IPL was conducted. Also, the significance of various supervised ML algorithms was investigated. Understanding the optical signature of cumulative inherent speckle of OCT scans arising from apoptotic retinal ganglion cells and photoreceptors may provide vital information for the prevention of retinal neurodegeneration

Automated Segmentation of Retinal Optical Coherence Tomography Images

Aim. Optical Coherence Tomography (OCT) is a fast and non-invasive medical imaging technique which helps in the investigation of each individual retinal layer structure. For early detection of retinal diseases and the study of their progression, segmentation of the OCT images into the distinct layers of the retina plays a crucial role. However, segmentation done by the clinicians manually is extremely tedious, time-consuming and variable with respect to the expertise level. Hence, there is an utmost necessity to develop an automated segmentation algorithm for retinal OCT images which is fast, accurate, and eases clinical decision making. Methods. Graph-theoretical methods have been implemented to develop an automated segmentation algorithm for spectral domain OCT (SD-OCT) images of the retina. As a pre-processing step, the best method for denoising the SD-OCT images prior to graph-based segmentation was determined by comparison between simple Gaussian filtering and an advanced wavelet-based denoising technique. A shortest-path based graph search technique was implemented to accurately delineate intra-retinal layer boundaries within the SD-OCT images. The results from the automated algorithm were also validated by comparison with manual segmentation done by an expert clinician using a specially designed graphical user interface (GUI). Results. The algorithm delineated seven intra-retinal boundaries thereby segmenting six layers of the retina along with computing their thicknesses. The thickness results from the automated algorithm when compared to normative layer thickness values from a published study showed no significant differences (p > 0.05) for all layers except layer 4 (p = 0.04). Furthermore, when a comparative analysis was done between the results from the automated segmentation algorithm and that from manual segmentation by an expert, the accuracy of the algorithm varied between 74.58% (layer 2) to 98.90% (layer 5). Additionally, the comparison of two different denoising techniques revealed that there was no significant impact of an advanced wavelet-based denoising technique over the use of simple Gaussian filtering on the accuracy of boundary detection by the graph-based algorithm. Conclusion. An automated graph-based algorithm was developed and implemented in this thesis for the segmentation of seven intra-retinal boundaries and six layers in SD-OCT images which is as good as manual segmentation by an expert clinician. This thesis also concludes on the note that simple Gaussian filters are sufficient to denoise the images in graph-based segmentation techniques and does not require an advanced denoising technique. This makes the complexity of implementation far more simple and efficient in terms of time and memory requirements

Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer

Den weltweiten Goldstandard zur Dignitätsbestimmung auffälliger Schleimhaut-läsionen des oberen Aerodigestivtraktes (OADT) stellt die invasive Entnahme von Gewebeproben zur Begutachtung durch einen Pathologen dar. Lässt sich histologisch ein maligner Tumor nachweisen, handelt es sich in über 90% der Fälle um ein Plattenepithelkarzinom (PEC) der Schleimhäute (Pai und Westra 2009). Die visuelle und endoskopische Untersuchung erfolgt aktuell sowohl ambulant als auch während Tumoroperationen im klinischen Alltag nur mit Weißlicht. Eine langjährige klinische Erfahrung und genaue Kenntnis der Anatomie sind daher zwingend notwendig, da eine frühzeitige Diagnose entscheidend für die Behandlungsstrategie und die Chancen auf Heilung der Patienten ist. Es handelt sich hier um eine stark untersucherabhängige Methode, die keine unmittelbare histologische Aussage zu Schleimhautveränderungen im OADT treffen kann (Ambrosch 1996). Deshalb werden seit Jahrzehnten weltweit verschiedene innovative optische Bildgebungsverfahren in der Hals-, Nasen- und Ohrenheilkunde (HNO-Heilkunde) zur besseren Detektion und Abgrenzung von Tumoren entwickelt. Das ideale Ziel der einzelnen Verfahren ist non-invasiv und in Echtzeit im Sinne einer „optischen Biopsie“ während ambulanter Untersuchungen oder bei Operationen definitive Aussagen über Gewebeveränderungen zu treffen (Volgger et al. 2013a, Arens et al. 2016). Bisher wird noch kein optisches Diagnoseverfahren im klinischen Alltag angewendet (Betz et al. 2016). Eine relativ neue Technik stellt in diesem Zusammenhang die konfokale Endomikroskopie (CLE) dar. Im Vergleich zu anderen Fachdisziplinen wie beispielsweise der Gastroenterologie wurden in der HNO-Heilkunde nur wenige Arbeiten publiziert, die die CLE zur Erkennung von PEC verwendet (Abbaci et al. 2014, Goetz et al. 2011). Es wurde bisher gezeigt, dass diese optische Technik zu diesem Zweck ein gewisses Potential besitzt. Quantitativ messbare Kriterien, die eine eindeutige Unterscheidung zwischen Tumorgewebe und gesunder Schleimhaut ermöglich, wurden aber noch nicht bestimmt (Thong et al. 2012, Volgger et al. 2013a). Unabhängig von einander kommen verschiedene Studien zu dem Schluss, dass bei der Betrachtung von CLE-Aufnahmen Unterschiede in der Architektur der Zellverbände und in der Zellgröße von Tumoren im Vergleich zu gesunder Schleimhaut auffallend sind (Pogorzelski et al. 2012, Haxel et al. 2010). In der zugrunde liegenden publizierten Orginalarbeit wird unseres Wissens der weltweit erste automatisierte Bilderkennungsalgorithmus zur Detektion von PEC im OADT anhand von CLEBilder vorgestellt. Die vorgelegte Arbeit ist zudem die weltweit erste Publikation, die quantitativ messbare Bilddaten in CLE-Bildern erhebt. Sie beweist, dass sowohl die Architektur der oberflächlichen Zellverbände als auch die Zellgröße in CLE-Bilder valide Kriterien sind, anhand derer ein PEC von gesunder Schleimhaut unterschieden werden kann. Darüber hinaus wurden bei der Studie indirekt zahlreiche Daten über die generelle Zellgröße und Gewebestruktur von PEC und gesunder Schleimhaut des OADT erhoben. Die prospektive Observationsstudie wurde in der Klinik für Hals-Nasen-Ohrenheilkunde am Universitätsklinikum Jena durchgeführt. Teilnehmer der Studie waren 12 Patienten mit klinischem Verdacht eines PEC. Die CLE-Bilder wurden nach intravenöser (i. v.) Applikation von Fluorescein in vivo während diagnostischer Panendoskopien aufgezeichnet. An allen untersuchten Schleimhautläsionen wurden direkt im Anschluss Biopsien entnommen. Zwei Gruppen mit einerseits histologischem Nachweis eines PEC (Tumorgruppe n=5) und andererseits mit gesunder Schleimhaut (Kontrollgruppe n=7) wurden gebildet. Die Auswertung der CLE-Aufnahmen sowie die Annotation relevanter Bildsequenzen und Bildareale erfolgte mit medizinischem Expertenwissen. Darauf aufbauend wurden im nächsten Schritt mit Methoden der digitalen Bilderkennung quantitativ messbare Bilddaten identifiziert. Die Analyse mit spezifischen Bilderkennungsverfahren („automated cell border segmentation, distance map“) ergab statistische Werte der Zellgrößen in den beiden Gruppen. Anhand dieser Informationen erfolgte das Training des Algorithmus mit der „leave-two-patients-out“- Methode (Hyperlink zum öffentlich zugänglichen technischen Report: http://www.inf-cv.unijena. de/microscopyanalysis). Unser Algorithmus ist in der Lage mit einer Spezifität von 0.85 ± 0.14 und einer Sensitivität von 0.72 ± 0.13 CLE-Bilder von PEC von gesunder Schleimhaut zu unterscheiden. Um die Aussagen des Algorithmus korrekt zu bewerten ist bei der Anwendung dieses optischen Verfahrens medizinisches Expertenwissen notwendig. Die Weiterentwicklung zur „online“-Anwendung im Sinne einer „optischen Biopsie“ als Ergänzung zur Weißlichtuntersuchung erscheint realistisch, wenn größere klinische Studien folgen