27 research outputs found
Retinal Imaging in Alzheimer’s Disease
Identifying biomarkers of Alzheimer's disease (AD) will accelerate the understanding of its pathophysiology, facilitate screening and risk stratification, and aid in developing new therapies. Developments in non-invasive retinal imaging technologies, including optical coherence tomography (OCT), OCT angiography and digital retinal photography, have provided a means to study neuronal and vascular structures in the retina in people with AD. Both qualitative and quantitative measurements from these retinal imaging technologies (eg, thinning of peripapillary retinal nerve fibre layer, inner retinal layer, and choroidal layer, reduced capillary density, abnormal vasodilatory response) have been shown to be associated with cognitive function impairment and risk of AD. The development of computer algorithms for respective retinal imaging methods has further enhanced the potential of retinal imaging as a viable tool for rapid, early detection and screening of AD. In this review, we present an update of current retinal imaging techniques and their potential applications in AD research. We also discuss the newer retinal imaging techniques and future directions in this expanding field
Human retinal oximetry using spectral imaging
The principal aim of the research described in this thesis was to develop a technique
of non-invasively measuring the oxygen saturation within the retinal vasculature of human
subjects (retinal oximetry). The evaluation of a hyperspectral fundus camera used to acquire
retinal images in different wavelengths of visible light, and the image analysis techniques
used to perform retinal oximetry are described.
Validation of the oximetry techniques was performed using an artificial eye
containing human blood of known oxygen saturation: the calculated oxygen saturation was
compared to the gold standard measurement. The mean differences between the calculated
and measured oxygen saturations were small.
Hyperspectral imaging/oximetry of normal subjects was performed to characterize the
oximetric features of the retinal vasculature. The mean oxygen saturation (± SD) of the
temporal retinal arterioles and venules were 110.8% (± 11.8%) and 27.7% (± 3.2%)
respectively.
The application of the retinal oximetry technique was explored in patients with retinal
arterial and venous occlusion to determine whether oximetric changes in the retinal
vasculature could be detected. Variation in measured oxygen saturation of the retinal
arterioles and venules respectively were apparent, and corresponded with angiographic
features of retinal capillary loss.
The techniques were applied to patients with asymmetrical primary open angle
glaucoma to determine whether oximetric changes could be detected. The mean oxygen
saturation of the temporal retinal venules were significantly higher [44.8% (± 24.2%)] in the
more advanced glaucomatous eyes compared to normal subjects. Hyperoxia of the retinal
venules suggests reduced oxygen consumption as a consequence of inner retinal dysfunction
in glaucoma. However, because of the small sample size, further research on a larger
population of subjects is required to support this finding.
Hyperspectral imaging could be used to detect oximetric abnormalities in the retinal
vasculature in patients with retinovascular occlusion and glaucoma
Human retinal oximetry using hyperspectral imaging
The aim of the work reported in this thesis was to investigate the possibility of
measuring human retinal oxygen saturation using hyperspectral imaging. A direct
non-invasive quantitative mapping of retinal oxygen saturation is enabled by
hyperspectral imaging whereby the absorption spectra of oxygenated and deoxygenated
haemoglobin are recorded and analysed. Implementation of spectral
retinal imaging thus requires ophthalmic instrumentation capable of efficiently
recording the requisite spectral data cube. For this purpose, a spectral retinal imager
was developed for the first time by integrating a liquid crystal tuneable filter into the
illumination system of a conventional fundus camera to enable the recording of
narrow-band spectral images in time sequence from 400nm to 700nm. Postprocessing
algorithms were developed to enable accurate exploitation of spectral
retinal images and overcome the confounding problems associated with this technique
due to the erratic eye motion and illumination variation.
Several algorithms were developed to provide semi-quantitative and quantitative
oxygen saturation measurements. Accurate quantitative measurements necessitated an
optical model of light propagation into the retina that takes into account the
absorption and scattering of light by red blood cells. To validate the oxygen saturation
measurements and algorithms, a model eye was constructed and measurements were
compared with gold-standard measurements obtained by a Co-Oximeter. The
accuracy of the oxygen saturation measurements was (3.31%± 2.19) for oxygenated
blood samples. Clinical trials from healthy and diseased subjects were analysed and
oxygen saturation measurements were compared to establish a merit of certain retinal
diseases. Oxygen saturation measurements were in agreement with clinician
expectations in both veins (48%±9) and arteries (96%±5). We also present in this
thesis the development of novel clinical instrument based on IRIS to perform retinal
oximetry.Al-baath University, Syri
Computational Analysis of Fundus Images: Rule-Based and Scale-Space Models
Fundus images are one of the most important imaging examinations in modern ophthalmology
because they are simple, inexpensive and, above all, noninvasive.
Nowadays, the acquisition and
storage of highresolution
fundus images is relatively easy and fast. Therefore, fundus imaging
has become a fundamental investigation in retinal lesion detection, ocular health monitoring and
screening programmes. Given the large volume and clinical complexity associated with these images,
their analysis and interpretation by trained clinicians becomes a timeconsuming
task and is
prone to human error. Therefore, there is a growing interest in developing automated approaches
that are affordable and have high sensitivity and specificity. These automated approaches need to
be robust if they are to be used in the general population to diagnose and track retinal diseases. To
be effective, the automated systems must be able to recognize normal structures and distinguish
them from pathological clinical manifestations.
The main objective of the research leading to this thesis was to develop automated systems capable
of recognizing and segmenting retinal anatomical structures and retinal pathological clinical
manifestations associated with the most common retinal diseases. In particular, these automated
algorithms were developed on the premise of robustness and efficiency to deal with the difficulties
and complexity inherent in these images. Four objectives were considered in the analysis of
fundus images. Segmentation of exudates, localization of the optic disc, detection of the midline
of blood vessels, segmentation of the vascular network and detection of microaneurysms.
In addition, we also evaluated the detection of diabetic retinopathy on fundus images using the
microaneurysm detection method. An overview of the state of the art is presented to compare the
performance of the developed approaches with the main methods described in the literature for
each of the previously described objectives. To facilitate the comparison of methods, the state of
the art has been divided into rulebased
methods and machine learningbased
methods.
In the research reported in this paper, rulebased
methods based on image processing methods
were preferred over machine learningbased
methods. In particular, scalespace
methods proved
to be effective in achieving the set goals.
Two different approaches to exudate segmentation were developed. The first approach is based on
scalespace
curvature in combination with the local maximum of a scalespace
blob detector and
dynamic thresholds. The second approach is based on the analysis of the distribution function of
the maximum values of the noise map in combination with morphological operators and adaptive
thresholds. Both approaches perform a correct segmentation of the exudates and cope well with
the uneven illumination and contrast variations in the fundus images.
Optic disc localization was achieved using a new technique called cumulative sum fields, which was
combined with a vascular enhancement method. The algorithm proved to be reliable and efficient,
especially for pathological images. The robustness of the method was tested on 8 datasets.
The detection of the midline of the blood vessels was achieved using a modified corner detector
in combination with binary philtres and dynamic thresholding. Segmentation of the vascular network
was achieved using a new scalespace
blood vessels enhancement method. The developed
methods have proven effective in detecting the midline of blood vessels and segmenting vascular
networks.
The microaneurysm detection method relies on a scalespace
microaneurysm detection and labelling
system. A new approach based on the neighbourhood of the microaneurysms was used
for labelling. Microaneurysm detection enabled the assessment of diabetic retinopathy detection.
The microaneurysm detection method proved to be competitive with other methods, especially with highresolution
images. Diabetic retinopathy detection with the developed microaneurysm
detection method showed similar performance to other methods and human experts.
The results of this work show that it is possible to develop reliable and robust scalespace
methods
that can detect various anatomical structures and pathological features of the retina. Furthermore,
the results obtained in this work show that although recent research has focused on machine learning
methods, scalespace
methods can achieve very competitive results and typically have greater
independence from image acquisition. The methods developed in this work may also be relevant
for the future definition of new descriptors and features that can significantly improve the results
of automated methods.As imagens do fundo do olho são hoje um dos principais exames imagiológicos da oftalmologia
moderna, pela sua simplicidade, baixo custo e acima de tudo pelo seu carácter nãoinvasivo.
A
aquisição e armazenamento de imagens do fundo do olho com alta resolução é também relativamente
simples e rápida. Desta forma, as imagens do fundo do olho são um exame fundamental
na identificação de alterações retinianas, monitorização da saúde ocular, e em programas de rastreio.
Considerando o elevado volume e complexidade clínica associada a estas imagens, a análise
e interpretação das mesmas por clínicos treinados tornase
uma tarefa morosa e propensa a erros
humanos. Assim, há um interesse crescente no desenvolvimento de abordagens automatizadas,
acessíveis em custo, e com uma alta sensibilidade e especificidade. Estas devem ser robustas para
serem aplicadas à população em geral no diagnóstico e seguimento de doenças retinianas. Para
serem eficazes, os sistemas de análise têm que conseguir detetar e distinguir estruturas normais
de sinais patológicos.
O objetivo principal da investigação que levou a esta tese de doutoramento é o desenvolvimento
de sistemas automáticos capazes de detetar e segmentar as estruturas anatómicas da retina, e os
sinais patológicos retinianos associados às doenças retinianas mais comuns. Em particular, estes
algoritmos automatizados foram desenvolvidos segundo as premissas de robustez e eficácia para
lidar com as dificuldades e complexidades inerentes a estas imagens.
Foram considerados quatro objetivos de análise de imagens do fundo do olho. São estes, a segmentação
de exsudados, a localização do disco ótico, a deteção da linha central venosa dos vasos
sanguíneos e segmentação da rede vascular, e a deteção de microaneurismas. De acrescentar que
usando o método de deteção de microaneurismas, avaliouse
também a capacidade de deteção da
retinopatia diabética em imagens do fundo do olho.
Para comparar o desempenho das metodologias desenvolvidas neste trabalho, foi realizado um
levantamento do estado da arte, onde foram considerados os métodos mais relevantes descritos na
literatura para cada um dos objetivos descritos anteriormente. Para facilitar a comparação entre
métodos, o estado da arte foi dividido em metodologias de processamento de imagem e baseadas
em aprendizagem máquina.
Optouse
no trabalho de investigação desenvolvido pela utilização de metodologias de análise espacial
de imagem em detrimento de metodologias baseadas em aprendizagem máquina. Em particular,
as metodologias baseadas no espaço de escalas mostraram ser efetivas na obtenção dos
objetivos estabelecidos.
Para a segmentação de exsudados foram usadas duas abordagens distintas. A primeira abordagem
baseiase
na curvatura em espaço de escalas em conjunto com a resposta máxima local de um detetor
de manchas em espaço de escalas e limiares dinâmicos. A segunda abordagem baseiase
na
análise do mapa de distribuição de ruído em conjunto com operadores morfológicos e limiares
adaptativos. Ambas as abordagens fazem uma segmentação dos exsudados de elevada precisão,
além de lidarem eficazmente com a iluminação nãouniforme
e a variação de contraste presente
nas imagens do fundo do olho. A localização do disco ótico foi conseguida com uma nova técnica
designada por campos de soma acumulativos, combinada com métodos de melhoramento da rede
vascular. O algoritmo revela ser fiável e eficiente, particularmente em imagens patológicas. A robustez
do método foi verificada pela sua avaliação em oito bases de dados. A deteção da linha central
dos vasos sanguíneos foi obtida através de um detetor de cantos modificado em conjunto com
filtros binários e limiares dinâmicos. A segmentação da rede vascular foi conseguida com um novo
método de melhoramento de vasos sanguíneos em espaço de escalas. Os métodos desenvolvidos mostraram ser eficazes na deteção da linha central dos vasos sanguíneos e na segmentação da rede
vascular. Finalmente, o método para a deteção de microaneurismas assenta num formalismo de
espaço de escalas na deteção e na rotulagem dos microaneurismas. Para a rotulagem foi utilizada
uma nova abordagem da vizinhança dos candidatos a microaneurismas. A deteção de microaneurismas
permitiu avaliar também a deteção da retinopatia diabética. O método para a deteção
de microaneurismas mostrou ser competitivo quando comparado com outros métodos, em particular
em imagens de alta resolução. A deteção da retinopatia diabética exibiu um desempenho
semelhante a outros métodos e a especialistas humanos.
Os trabalhos descritos nesta tese mostram ser possível desenvolver uma abordagem fiável e robusta
em espaço de escalas capaz de detetar diferentes estruturas anatómicas e sinais patológicos
da retina.
Além disso, os resultados obtidos mostram que apesar de a pesquisa mais recente concentrarse
em metodologias de aprendizagem máquina, as metodologias de análise espacial apresentam
resultados muito competitivos e tipicamente independentes do equipamento de aquisição das imagens.
As metodologias desenvolvidas nesta tese podem ser importantes na definição de novos
descritores e características, que podem melhorar significativamente o resultado de métodos automatizados
Automatic analysis of retinal images to aid in the diagnosis and grading of diabetic retinopathy
Diabetic retinopathy (DR) is the most common complication of diabetes mellitus and one of the leading causes of preventable blindness in the adult working population. Visual loss can be prevented from the early stages of DR, when the treatments are effective. Therefore, early diagnosis is paramount. However, DR may be clinically asymptomatic until the advanced stage, when vision is already affected and treatment may become difficult. For this reason, diabetic patients should undergo regular eye examinations through screening programs. Traditionally, DR screening programs are run by trained specialists through visual inspection of the retinal images. However, this manual analysis is time consuming and expensive. With the increasing incidence of diabetes and the limited number of clinicians and sanitary resources, the early detection of DR becomes non-viable. For this reason, computed-aided diagnosis (CAD) systems are required to assist specialists for a fast, reliable diagnosis, allowing to reduce the workload and the associated costs.
We hypothesize that the application of novel, automatic algorithms for fundus image analysis could contribute to the early diagnosis of DR. Consequently, the main objective of the present Doctoral Thesis is to study, design and develop novel methods based on the automatic analysis of fundus images to aid in the screening, diagnosis, and treatment of DR.
In order to achieve the main goal, we built a private database and used five retinal public databases: DRIMDB, DIARETDB1, DRIVE, Messidor and Kaggle. The stages of fundus image processing covered in this Thesis are: retinal image quality assessment (RIQA), the location of the optic disc (OD) and the fovea, the segmentation of RLs and EXs, and the DR severity grading.
RIQA was studied with two different approaches. The first approach was based on the combination of novel, global features. Results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity using the private database. We developed a second approach aimed at RIQA based on deep learning. We achieved 95.29% accuracy with the private database and 99.48% accuracy with the DRIMDB database.
The location of the OD and the fovea was performed using a combination of saliency maps. The proposed methods were evaluated over the private database and the public databases DRIVE, DIARETDB1 and Messidor. For the OD, we achieved 100% accuracy for all databases except Messidor (99.50%). As for the fovea location, we also reached 100% accuracy for all databases except Messidor (99.67%).
The joint segmentation of RLs and EXs was accomplished by decomposing the fundus image into layers. Results were computed per pixel and per image. Using the private database, 88.34% per-image accuracy (ACCi) was reached for the RL detection and 95.41% ACCi for EX detection. An additional method was proposed for the segmentation of RLs based on superpixels. Evaluating this method with the private database, we obtained 84.45% ACCi. Results were validated using the DIARETDB1 database.
Finally, we proposed a deep learning framework for the automatic DR severity grading. The method was based on a novel attention mechanism which performs a separate attention of the dark and the bright structures of the retina. The Kaggle DR detection dataset was used for development and validation. The International Clinical DR Scale was considered, which is made up of 5 DR severity levels. Classification results for all classes achieved 83.70% accuracy and a Quadratic Weighted Kappa of 0.78.
The methods proposed in this Doctoral Thesis form a complete, automatic DR screening system, contributing to aid in the early detection of DR. In this way, diabetic patients could receive better attention for their ocular health avoiding vision loss. In addition, the workload of specialists could be relieved while healthcare costs are reduced.La retinopatía diabética (RD) es la complicación más común de la diabetes mellitus y una de las principales causas de ceguera prevenible en la población activa adulta. El diagnóstico precoz es primordial para prevenir la pérdida visual. Sin embargo, la RD es clínicamente asintomática hasta etapas avanzadas, cuando la visión ya está afectada. Por eso, los pacientes diabéticos deben someterse a exámenes oftalmológicos periódicos a través de programas de cribado. Tradicionalmente, estos programas están a cargo de especialistas y se basan de la inspección visual de retinografías. Sin embargo, este análisis manual requiere mucho tiempo y es costoso. Con la creciente incidencia de la diabetes y la escasez de recursos sanitarios, la detección precoz de la RD se hace inviable. Por esta razón, se necesitan sistemas de diagnóstico asistido por ordenador (CAD) que ayuden a los especialistas a realizar un diagnóstico rápido y fiable, que permita reducir la carga de trabajo y los costes asociados.
El objetivo principal de la presente Tesis Doctoral es estudiar, diseñar y desarrollar nuevos métodos basados en el análisis automático de retinografías para ayudar en el cribado, diagnóstico y tratamiento de la RD. Las etapas estudiadas fueron: la evaluación de la calidad de la imagen retiniana (RIQA), la localización del disco óptico (OD) y la fóvea, la segmentación de RL y EX y la graduación de la severidad de la RD.
RIQA se estudió con dos enfoques diferentes. El primer enfoque se basó en la combinación de características globales. Los resultados lograron una precisión del 91,46% utilizando la base de datos privada. El segundo enfoque se basó en aprendizaje profundo. Logramos un 95,29% de precisión con la base de datos privada y un 99,48% con la base de datos DRIMDB.
La localización del OD y la fóvea se realizó mediante una combinación de mapas de saliencia. Los métodos propuestos fueron evaluados sobre la base de datos privada y las bases de datos públicas DRIVE, DIARETDB1 y Messidor. Para el OD, logramos una precisión del 100% para todas las bases de datos excepto Messidor (99,50%). En cuanto a la ubicación de la fóvea, también alcanzamos un 100% de precisión para todas las bases de datos excepto Messidor (99,67%).
La segmentación conjunta de RL y EX se logró descomponiendo la imagen del fondo de ojo en capas. Utilizando la base de datos privada, se alcanzó un 88,34% de precisión por imagen (ACCi) para la detección de RL y un 95,41% de ACCi para la detección de EX. Se propuso un método adicional para la segmentación de RL basado en superpíxeles. Evaluando este método con la base de datos privada, obtuvimos 84.45% ACCi. Los resultados se validaron utilizando la base de datos DIARETDB1.
Finalmente, propusimos un método de aprendizaje profundo para la graduación automática de la gravedad de la DR. El método se basó en un mecanismo de atención. Se utilizó la base de datos Kaggle y la Escala Clínica Internacional de RD (5 niveles de severidad). Los resultados de clasificación para todas las clases alcanzaron una precisión del 83,70% y un Kappa ponderado cuadrático de 0,78.
Los métodos propuestos en esta Tesis Doctoral forman un sistema completo y automático de cribado de RD, contribuyendo a ayudar en la detección precoz de la RD. De esta forma, los pacientes diabéticos podrían recibir una mejor atención para su salud ocular evitando la pérdida de visión. Además, se podría aliviar la carga de trabajo de los especialistas al mismo tiempo que se reducen los costes sanitarios.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione
Investigation of the Retinal Biomarkers of Alzheimer’s Disease and Atherosclerosis Using Hyperspectral Images
Le fait que l'oeil puisse être visualisé de manière non invasive ouvre des possibilités de mesure de biomarqueurs pour le diagnostic de conditions à long terme. Selon de nombreuses études, plusieurs maladies cardiovasculaires et neurodégénératives telles que la maladie d’Alzheimer (AD) et l’athérosclérose (ATH) se manifestent dans la rétine sous forme de modifications morphologiques pathologiques et / ou vasculaires. Des méthodes d'imagerie oculaire en deux dimensions et des techniques de tomographie par cohérence optique (OCT) en trois dimensions ont été développées pour fournir des descriptions des structures rétiniennes. Cependant, les images acquises par ces techniques permettent principalement de mesurer les caractéristiques spatiales et pas la variance relative de l’intensité des pixels sur différentes longueurs d’onde, de sorte que d’importantes caractéristiques liées aux tissus peuvent encore rester à découvrir.
Dans cette étude, une caméra rétinienne métabolique hyperspectrale (MHRC) a été utilisée pour permettre l'acquisition d'une série d'images rétiniennes obtenues à des longueurs d'onde spécifiques couvrant le spectre du visible au proche infrarouge (NIR). Dans cette technique, le facteur de transmission, l'absorption et la diffusion de la lumière sont reflétés dans le spectre de la lumière émise par le tissu. Par conséquent, non seulement les caractéristiques spatiales communes mais également les « signatures spectrales » de biomolécules pourraient être révélées. Cela aide à trouver une plus grande variété de caractéristiques spatiales / spectrales pour une investigation plus précise des biomarqueurs rétiniens des maladies.
En ce qui concerne les coûts et les limites associés aux diagnostics actuels de l’AD et de l’ATH, le but de cette thèse était d’analyser le contenu en informations d’images rétiniennes hyperspectrales riches en données dans le but de caractériser des informations discriminantes cachées liées aux tissus afin d’identifier des biomarqueurs possibles de ces deux maladies. À cette fin, une combinaison de caractéristiques vasculaires et de mesures de textures spatiales-spectrales ont été extraites de différentes régions anatomiques de la rétine.
Dans le contexte de la maladie d'Alzheimer, des images rétiniennes de 20 cas présentant une altération cognitive et de 26 cas normaux cognitivement ont été acquises à l'aide de la caméra MHRC. Le statut amyloïde cérébral a été déterminé à partir de lectures binaires effectuées par un panel de 3 experts noteurs ayant participé à des études de TEP au 18F-Florbetaben.
Des caractéristiques de l’image rétinienne ont été calculées, notamment la tortuosité et le diamètre des vaisseaux, ainsi que les mesures de textures spatiales-spectrales sur les artérioles, les veinules et le tissu environnant. Les veinules rétiniennes des sujets amyloïdes positifs (Aβ +) ont présenté une tortuosité moyenne plus élevée par rapport aux sujets amyloïdes négatifs (Aβ-). Le diamètre artériolaire des sujets Aβ + s'est avéré supérieur à celui des sujets Aβ- dans une zone adjacente à la tête du nerf optique. De plus, une différence significative entre les mesures de texture construites sur les artérioles rétiniennes et leurs régions adjacentes a été observée chez les sujets Aβ + par rapport aux Aβ-.
Dans le contexte de l'ATH, 60 images rétiniennes de 30 ATH probables sur le plan clinique et 30 cas de contrôle ont été acquises. Les critères d'inclusion pour les sujets souffrant d'ATH comprenaient: l'infarctus du myocarde; angiographie coronaire montrant au moins une sténose coronaire (plus de 50%); et / ou une angioplastie coronaire; et /ou pontage coronaire.
Les artérioles rétiniennes des sujets ATH ont montré un rétrécissement significatif par rapport aux sujets témoins. En outre, une différence significative entre les mesures de textures d'images prises sur les artérioles et les veinules rétiniennes et leurs régions adjacentes a été trouvée entre les sujets ATH et les sujets témoins.
Nos études transversales ont montré que l’analyse hyperspectrale des images rétiniennes pouvait discerner avec une précision acceptable l’AD et l’ATH des sujets témoins correspondants.----------ABSTRACT
The fact that eye can be visualized non-invasively, opens up possibilities to measure biomarkers for diagnosis of long-term conditions. A significant body of literature has demonstrated that many of the neurodegenerative and cardiovascular diseases such as Alzheimer’s disease (AD) and atherosclerosis (ATH) manifest themselves in retina as pathological and/or vasculature morphological changes. Methods for two-dimensional fundus imaging and techniques for three-dimensional optical coherence tomography (OCT) have been developed to provide descriptions of retinal structures. However, images acquired by these techniques mostly allow for measuring the spatial characteristics of the tissue and lack of the relative variances across differing wavelengths, thus important spectral features may remain uncovered.
In this study, a Metabolic Hyperspectral Retinal Camera (MHRC) was used that permits the acquisition of a series of retinal images obtained at specific wavelengths covering the visible and near infrared (NIR) spectrum. In this technique, light transmittance, absorption, and scatter are reflected in the spectrum of light emitted from the tissue. Use of MHRC in this study was aimed to extract not only the common spatial features but also “spectral signatures” of biomolecules in retinal tissue.
Regarding the costs and limitations of the current diagnostic methods for AD and ATH, the purpose of this thesis was to analyze the information content of data-rich hyperspectral retinal images to characterize tissue-related discriminatory information to identify possible biomarkers of Alzheimer’s disease and atherosclerosis. To this end, a combination of vascular features and spatial/spectral texture measures were extracted from different anatomical regions of the retina.
In the context of AD, retinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired using MHRC. The cerebral amyloid status was determined from binary reads by a panel of three expert raters on 18F-Florbetaben PET studies. Our approach did not aim to visualize directly Aβ deposits in the retina but rather to determine a likely amyloid status based on sets of retinal image features highly correlated with the cerebral amyloid status.
Retinal image features were calculated including vessels’ tortuosity and diameter. Spatial/spectral texture measures over arterioles, venules, and tissue around were also extracted. Retinal venules of amyloid positive subjects (Aβ+) showed a higher mean tortuosity compared to the amyloid negative (Aβ-) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ- subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between spatial/spectral texture measures built over retinal arterioles and surrounding tissues were observed in Aβ+ subjects when compared to the Aβ-.
In the context of ATH, 60 retinal images from 30 clinically probable ATH and 30 control cases were acquired. Inclusion criteria for subjects suffering from ATH included: myocardial infarction; coronary angiography showing at least one coronary stenosis (more than 50%); and/or coronary angioplasty; and/or coronary bypass.
Retinal arterioles of ATH subjects showed a significant narrowing when compared to control subjects. Moreover, a significant difference between image texture measures taken over retinal arterioles and retinal venules and their adjacent regions was observed between ATH subjects and control subjects.
Our cross-sectional studies have shown that hyperspectral retinal image analysis could be used to discriminate AD and ATH from corresponding control subjects based on a non-invasive eye scan
A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography
Retinal optical coherence tomography (OCT) and optical coherence tomography
angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's
disease (AD). These non-invasive imaging techniques are cost-effective and more
accessible than alternative neuroimaging tools. However, interpreting and
classifying multi-slice scans produced by OCT devices is time-consuming and
challenging even for trained practitioners.
There are surveys on machine learning and deep learning approaches concerning
the automated analysis of OCT scans for various diseases such as glaucoma.
However, the current literature lacks an extensive survey on the diagnosis of
Alzheimer's disease or cognitive impairment using OCT or OCTA. This has
motivated us to do a comprehensive survey aimed at machine/deep learning
scientists or practitioners who require an introduction to the problem. The
paper contains 1) an introduction to the medical background of Alzheimer's
Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging
modalities, 2) a review of various technical proposals for the problem and the
sub-problems from an automated analysis perspective, 3) a systematic review of
the recent deep learning studies and available OCT/OCTA datasets directly aimed
at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the
latter, we used Publish or Perish Software to search for the relevant studies
from various sources such as Scopus, PubMed, and Web of Science. We followed
the PRISMA approach to screen an initial pool of 3073 references and determined
ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis.
We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as
the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept,
methodology, invest, data curation, and writing org.draft by Yasemin Turkan.
Concept, method, writing review editing, and supervision by F. Boray Te
Novel methods in retinal vessel calibre feature extraction for systemic disease assessment
Retina and its vascular network have unique branching characteristics morphology of which will change as a result of some systemic diseases, including hypertension, stroke and diabetes. Therefore, retinal image has been used as non-invasive screening window for risk assessment and prediction of such disease condition especially at the baseline. The assessment is based on a number of features among which vessel diameter (both individual and summary) and fractal dimension (FD) are the ones mostly associated with risk of diabetes and stroke. The association is linked to the higher risk of diabetes and stroke in people with narrower retinal arteriole diameter or change in overall fractal dimension independent of any risk factor (i.e. blood pressure, cardiovascular risk factors). Diameter measurement requires vessel edges to be located and tracked however; accurate edge perception is subject to image contrast, shadows, lighting condition and even presence of retinopathy legions close to vessel boundaries. This will lead to imprecision and inconsistencies between different automatic measurement techniques and may affect the significance of its association with disease condition in risk-assessment studies. As accuracy and success of diameter measurement is subject to large variations due to image artifacts it may not be suitable for fully automatic applications. In order to compensate for such error, at first two novel automatic vessel diameter measurement techniques were proposed and validated which were more robust in the presence of such image artifacts compared to similar methods. However, sometimes the exact edge location and actual diameter value is not of interest. In most case-control studies, it is of importance to comparatively evaluate the variations in retinal vessel diameter as a sign of retinopathy such as arteriolar nicking as an example of hypertensive retinopathy. Vessel diameter is often required to be compared with a reference value in many analytical assessments for diagnostic purpose. This includes monitoring the diameter variations of a specific vessel segment within single subject overtime or across multiple subjects. This helps ophthalmologists to understand whether it has undergone any significant change and perhaps associate it with a disease abnormality. A technique that can effectively quantify that change without being impaired by image artifacts is of more importance and one of the rationales of this study. This research hypothesized an edge independent solution for quantifying diameter variations when the actual diameter value is not required and proposed a new feature based on fractal analysis of vessel cross-section profile as a time series signal. This feature provides a link between FD as a global measure of the complexity and diameter variation as local property of a specific vessel segment. The clinical application of this feature has been validated on two population studies which showed promising result for assessment of mild non-proliferative diabetic retinopathy and 10-year stroke. This research work has also investigated whether the FD of retinal microvasculature would be affected by cyclic pulsations of retinal vessels and whether ECG synchronization is required prior to taking fundus images to compensate for this potential source of variations