25 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Review on retrospective procedures to correct retinal motion artefacts in OCT imaging
Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments
Etude des effets respectifs de l'âge et de l'hypertension sur l'anatomie et la fonction des artères centrales et périphériques
We’ve tried to decipher the respective roles of age and hypertension on anatomy and function of large and small arteries using new non invasive imaging techniques. Large arteries: Using Aortic MRI, we’ve shown the proeminent role of ageing on arterial stiffening as well as the parallel and combined influence of age and hypertension . Using radial artery tonometry, we’ve shown links between anatomy, function and atherosclerosis in a large population. This generates new hypothesis to link arterial function and cardiovascular risk. Retinal microcirculation: We’ve validated a new high resolution imaging technique : adaptive optics. We’ve confirmed in a large cohort of hypertensives the existence of an inward eutrophic remodeling. We’ve observed Blood pressure drop related short term vasodilatation without changes in wall cross section. Together with the relationship we also established between arteriolar remodeling and total peripheral resistances, our results advocate for an important part of functional remodeling in hypertension. With ageing, we’ve observed a parietal thickening with wall cross section increase without lumen modifications. We’ve also confirmed the presence of an hypertrophic remodeling in diabetic patients. In conclusion: We have validated and confirmed the added value of new arterial non invasive imaging techniques on large populations to study cardiovascular risk factors induced arterial remodelingNous avons cherché à discerner les rôles respectifs de l’âge et de l’hypertension sur l’anatomie et la fonction des grosses et des petites artères en utilisant de nouvelles techniques non invasive. Au niveau macrovasculaire : En utilisant l’IRM aortique nous avons montré le rôle proéminent de l’âge et observé l’influence parallèle et combinée de l’âge et de l’hypertension sur la diminution de la rigidité. En utilisant la tonométrie de l’artère radiale nous avons montré des liens entre anatomie, fonction et athérosclérose dans une large population suggérant de nouvelles hypothèses pour relier fonction artérielle et risque cardiovasculaire Au niveau microvasculaire rétinien: Nous avons validé une nouvelle technologie d’imagerie de haute résolution: l’optique adaptative. Nous avons confirmé dans une large cohorte d’hypertendus la présence d’un remodelage eutrophique. Nous avons observé des variations à court terme de la lumière sans modification de la surface sectionnelle en cas de diminution de pression. Conjugués à la corrélation que nous avons établie entre le remodelage rétinien et les résistances périphériques totales, nos résultats plaident pour une part importante de remodelage fonctionnel. Avec le vieillissement, nous avons décrit un épaississement pariétal avec augmentation de la surface sectionnelle sans modification de la lumière. Nous avons confirmé la présence d’un remodelage hypertrophique chez les patients diabétiques. En conclusion : Nous avons validé et montré l’apport de nouvelles techniques d’imagerie artérielle non invasives sur de larges populations pour étudier le remodelage artériel dû à l’âge et aux facteurs de risque cardiovasculaires
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
Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach
RÉSUMÉ
La rétine permet de visualiser facilement une partie du réseau vasculaire humain. Elle offre
ainsi un aperçu direct sur le développement et le résultat de certaines maladies liées au réseau
vasculaire dans son entier. Chaque complication visible sur la rétine peut avoir un impact sur
la capacité visuelle du patient. Les plus petits vaisseaux sanguins sont parmi les premières
structures anatomiques affectées par la progression d’une maladie, être capable de les analyser
est donc crucial. Les changements dans l’état, l’aspect, la morphologie, la fonctionnalité, ou
même la croissance des petits vaisseaux indiquent la gravité des maladies.
Le diabète est une maladie métabolique qui affecte des millions de personnes autour
du monde. Cette maladie affecte le taux de glucose dans le sang et cause des changements
pathologiques dans différents organes du corps humain. La rétinopathie diabétique décrit l’en-
semble des conditions et conséquences du diabète au niveau de la rétine. Les petits vaisseaux
jouent un rôle dans le déclenchement, le développement et les conséquences de la rétinopa-
thie. Dans les dernières étapes de cette maladie, la croissance des nouveaux petits vaisseaux,
appelée néovascularisation, présente un risque important de provoquer la cécité. Il est donc
crucial de détecter tous les changements qui ont lieu dans les petits vaisseaux de la rétine
dans le but de caractériser les vaisseaux sains et les vaisseaux anormaux. La caractérisation
en elle-même peut faciliter la détection locale d’une rétinopathie spécifique.
La segmentation automatique des structures anatomiques comme le réseau vasculaire est
une étape cruciale. Ces informations peuvent être fournies à un médecin pour qu’elles soient
considérées lors de son diagnostic. Dans les systèmes automatiques d’aide au diagnostic, le
rôle des petits vaisseaux est significatif. Ne pas réussir à les détecter automatiquement peut
conduire à une sur-segmentation du taux de faux positifs des lésions rouges dans les étapes
ultérieures. Les efforts de recherche se sont concentrés jusqu’à présent sur la localisation
précise des vaisseaux de taille moyenne. Les modèles existants ont beaucoup plus de difficultés
à extraire les petits vaisseaux sanguins. Les modèles existants ne sont pas robustes à la grande
variance d’apparence des vaisseaux ainsi qu’à l’interférence avec l’arrière-plan. Les modèles de
la littérature existante supposent une forme générale qui n’est pas suffisante pour s’adapter
à la largeur étroite et la courbure qui caractérisent les petits vaisseaux sanguins. De plus, le
contraste avec l’arrière-plan dans les régions des petits vaisseaux est très faible. Les méthodes
de segmentation ou de suivi produisent des résultats fragmentés ou discontinus. Par ailleurs,
la segmentation des petits vaisseaux est généralement faite aux dépends de l’amplification
du bruit. Les modèles déformables sont inadéquats pour segmenter les petits vaisseaux. Les
forces utilisées ne sont pas assez flexibles pour compenser le faible contraste, la largeur, et
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la variance des vaisseaux. Enfin, les approches de type apprentissage machine nécessitent un
entraînement avec une base de données étiquetée. Il est très difficile d’obtenir ces bases de
données dans le cas des petits vaisseaux.
Cette thèse étend les travaux de recherche antérieurs en fournissant une nouvelle mé-
thode de segmentation des petits vaisseaux rétiniens. La détection de ligne à échelles multiples
(MSLD) est une méthode récente qui démontre une bonne performance de segmentation dans
les images de la rétine, tandis que le vote tensoriel est une méthode proposée pour reconnecter
les pixels. Une approche combinant un algorithme de détection de ligne et de vote tensoriel est
proposée. L’application des détecteurs de lignes a prouvé son efficacité à segmenter les vais-
seaux de tailles moyennes. De plus, les approches d’organisation perceptuelle comme le vote
tensoriel ont démontré une meilleure robustesse en combinant les informations voisines d’une
manière hiérarchique. La méthode de vote tensoriel est plus proche de la perception humain
que d’autres modèles standards. Comme démontré dans ce manuscrit, c’est un outil pour
segmenter les petits vaisseaux plus puissant que les méthodes existantes. Cette combinaison
spécifique nous permet de surmonter les défis de fragmentation éprouvés par les méthodes de
type modèle déformable au niveau des petits vaisseaux. Nous proposons également d’utiliser
un seuil adaptatif sur la réponse de l’algorithme de détection de ligne pour être plus robuste
aux images non-uniformes. Nous illustrons également comment une combinaison des deux
méthodes individuelles, à plusieurs échelles, est capable de reconnecter les vaisseaux sur des
distances variables. Un algorithme de reconstruction des vaisseaux est également proposé.
Cette dernière étape est nécessaire car l’information géométrique complète est requise pour
pouvoir utiliser la segmentation dans un système d’aide au diagnostic.
La segmentation a été validée sur une base de données d’images de fond d’oeil à haute
résolution. Cette base contient des images manifestant une rétinopathie diabétique. La seg-
mentation emploie des mesures de désaccord standards et aussi des mesures basées sur la
perception. En considérant juste les petits vaisseaux dans les images de la base de données,
l’amélioration dans le taux de sensibilité que notre méthode apporte par rapport à la méthode
standard de détection multi-niveaux de lignes est de 6.47%. En utilisant les mesures basées
sur la perception, l’amélioration est de 7.8%.
Dans une seconde partie du manuscrit, nous proposons également une méthode pour
caractériser les rétines saines ou anormales. Certaines images contiennent de la néovascula-
risation. La caractérisation des vaisseaux en bonne santé ou anormale constitue une étape
essentielle pour le développement d’un système d’aide au diagnostic. En plus des défis que
posent les petits vaisseaux sains, les néovaisseaux démontrent eux un degré de complexité
encore plus élevé. Ceux-ci forment en effet des réseaux de vaisseaux à la morphologie com-
plexe et inhabituelle, souvent minces et à fortes courbures. Les travaux existants se limitent
viii
à l’utilisation de caractéristiques de premier ordre extraites des petits vaisseaux segmentés.
Notre contribution est d’utiliser le vote tensoriel pour isoler les jonctions vasculaires et d’uti-
liser ces jonctions comme points d’intérêts. Nous utilisons ensuite une statistique spatiale
de second ordre calculée sur les jonctions pour caractériser les vaisseaux comme étant sains
ou pathologiques. Notre méthode améliore la sensibilité de la caractérisation de 9.09% par
rapport à une méthode de l’état de l’art.
La méthode développée s’est révélée efficace pour la segmentation des vaisseaux réti-
niens. Des tenseurs d’ordre supérieur ainsi que la mise en œuvre d’un vote par tenseur via
un filtrage orientable pourraient être étudiés pour réduire davantage le temps d’exécution et
résoudre les défis encore présents au niveau des jonctions vasculaires. De plus, la caractéri-
sation pourrait être améliorée pour la détection de la rétinopathie proliférative en utilisant
un apprentissage supervisé incluant des cas de rétinopathie diabétique non proliférative ou
d’autres pathologies. Finalement, l’incorporation des méthodes proposées dans des systèmes
d’aide au diagnostic pourrait favoriser le dépistage régulier pour une détection précoce des
rétinopathies et d’autres pathologies oculaires dans le but de réduire la cessité au sein de la
population.----------ABSTRACT
As an easily accessible site for the direct observation of the circulation system, human retina
can offer a unique insight into diseases development or outcome. Retinal vessels are repre-
sentative of the general condition of the whole systematic circulation, and thus can act as
a "window" to the status of the vascular network in the whole body. Each complication on
the retina can have an adverse impact on the patient’s sight. In this direction, small vessels’
relevance is very high as they are among the first anatomical structures that get affected
as diseases progress. Moreover, changes in the small vessels’ state, appearance, morphology,
functionality, or even growth indicate the severity of the diseases.
This thesis will focus on the retinal lesions due to diabetes, a serious metabolic disease
affecting millions of people around the world. This disorder disturbs the natural blood glucose
levels causing various pathophysiological changes in different systems across the human body.
Diabetic retinopathy is the medical term that describes the condition when the fundus and
the retinal vessels are affected by diabetes. As in other diseases, small vessels play a crucial
role in the onset, the development, and the outcome of the retinopathy. More importantly,
at the latest stage, new small vessels, or neovascularizations, growth constitutes a factor of
significant risk for blindness. Therefore, there is a need to detect all the changes that occur
in the small retinal vessels with the aim of characterizing the vessels to healthy or abnormal.
The characterization, in turn, can facilitate the detection of a specific retinopathy locally,
like the sight-threatening proliferative diabetic retinopathy.
Segmentation techniques can automatically isolate important anatomical structures like
the vessels, and provide this information to the physician to assist him in the final decision. In
comprehensive systems for the automatization of DR detection, small vessels role is significant
as missing them early in a CAD pipeline might lead to an increase in the false positive rate
of red lesions in subsequent steps. So far, the efforts have been concentrated mostly on the
accurate localization of the medium range vessels. In contrast, the existing models are weak
in case of the small vessels. The required generalization to adapt an existing model does not
allow the approaches to be flexible, yet robust to compensate for the increased variability in
the appearance as well as the interference with the background. So far, the current template
models (matched filtering, line detection, and morphological processing) assume a general
shape for the vessels that is not enough to approximate the narrow, curved, characteristics
of the small vessels. Additionally, due to the weak contrast in the small vessel regions,
the current segmentation and the tracking methods produce fragmented or discontinued
results. Alternatively, the small vessel segmentation can be accomplished at the expense of
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background noise magnification, in the case of using thresholding or the image derivatives
methods. Furthermore, the proposed deformable models are not able to propagate a contour
to the full extent of the vasculature in order to enclose all the small vessels. The deformable
model external forces are ineffective to compensate for the low contrast, the low width, the
high variability in the small vessel appearance, as well as the discontinuities. Internal forces,
also, are not able to impose a global shape constraint to the contour that could be able to
approximate the variability in the appearance of the vasculature in different categories of
vessels. Finally, machine learning approaches require the training of a classifier on a labelled
set. Those sets are difficult to be obtained, especially in the case of the smallest vessels. In
the case of the unsupervised methods, the user has to predefine the number of clusters and
perform an effective initialization of the cluster centers in order to converge to the global
minimum.
This dissertation expanded the previous research work and provides a new segmentation
method for the smallest retinal vessels. Multi-scale line detection (MSLD) is a recent method
that demonstrates good segmentation performance in the retinal images, while tensor voting
is a method first proposed for reconnecting pixels. For the first time, we combined the
line detection with the tensor voting framework. The application of the line detectors has
been proved an effective way to segment medium-sized vessels. Additionally, perceptual
organization approaches like tensor voting, demonstrate increased robustness by combining
information coming from the neighborhood in a hierarchical way. Tensor voting is closer than
standard models to the way human perception functions. As we show, it is a more powerful
tool to segment small vessels than the existing methods. This specific combination allows us
to overcome the apparent fragmentation challenge of the template methods at the smallest
vessels. Moreover, we thresholded the line detection response adaptively to compensate for
non-uniform images. We also combined the two individual methods in a multi-scale scheme
in order to reconnect vessels at variable distances. Finally, we reconstructed the vessels
from their extracted centerlines based on pixel painting as complete geometric information
is required to be able to utilize the segmentation in a CAD system.
The segmentation was validated on a high-resolution fundus image database that in-
cludes diabetic retinopathy images of varying stages, using standard discrepancy as well as
perceptual-based measures. When only the smallest vessels are considered, the improve-
ments in the sensitivity rate for the database against the standard multi-scale line detection
method is 6.47%. For the perceptual-based measure, the improvement is 7.8% against the
basic method.
The second objective of the thesis was to implement a method for the characterization of
isolated retinal areas into healthy or abnormal cases. Some of the original images, from which
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these patches are extracted, contain neovascularizations. Investigation of image features
for the vessels characterization to healthy or abnormal constitutes an essential step in the
direction of developing CAD system for the automatization of DR screening. Given that the
amount of data will significantly increase under CAD systems, the focus on this category of
vessels can facilitate the referral of sight-threatening cases to early treatment. In addition
to the challenges that small healthy vessels pose, neovessels demonstrate an even higher
degree of complexity as they form networks of convolved, twisted, looped thin vessels. The
existing work is limited to the use of first-order characteristics extracted from the small
segmented vessels that limits the study of patterns. Our contribution is in using the tensor
voting framework to isolate the retinal vascular junctions and in turn using those junctions
as points of interests. Second, we exploited second-order statistics computed on the junction
spatial distribution to characterize the vessels as healthy or neovascularizations. In fact, the
second-order spatial statistics extracted from the junction distribution are combined with
widely used features to improve the characterization sensitivity by 9.09% over the state of
art.
The developed method proved effective for the segmentation of the retinal vessels. Higher
order tensors along with the implementation of tensor voting via steerable filtering could
be employed to further reduce the execution time, and resolve the challenges at vascular
junctions. Moreover, the characterization could be advanced to the detection of prolifera-
tive retinopathy by extending the supervised learning to include non-proliferative diabetic
retinopathy cases or other pathologies. Ultimately, the incorporation of the methods into
CAD systems could facilitate screening for the effective reduction of the vision-threatening
diabetic retinopathy rates, or the early detection of other than ocular pathologies
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
Generalizable automated pixel-level structural segmentation of medical and biological data
Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These
solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution.
This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D
structural segmentation in a more generalizable manner, yet has enough adaptability to address
a number of specific image modalities, spanning retinal funduscopy, sequential
fluorescein angiography and two-photon microscopy.
The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based
measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D.
To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective
RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-)
pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations.
Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional
exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this
into consideration, we introduce a 5D orientation mapping to capture these orientation properties.
This mapping is incorporated into the local feature map description prior to a learning
machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods.
For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces