Machine learning methods for the characterization and classification of complex data

This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografía de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalías que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topología de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatía diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anàlisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automàtic sense supervisió que ordena imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automàtica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà, sent útil, fins i tot, per a la detecció automàtica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un làser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat. Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version

Non uniformity: structural strategy for optimizing functionality in skeletal ligaments

Ligaments serve as compliant connectors between hard tissues. In that role, they function under various load regimes and directions. However, the 3D structure of ligaments is still considered uniform. The periodontal ligament connects the tooth to the bone and like other ligaments, it sustains different types of loads in various directions. Using the PDL as a model, and employing a fabricated motorized set-up in a microCT instrument, morphological automated segmentation methods and 2nd harmonic generation imaging, we demonstrate that the fibrous network structure within the PDL is not uniform, even before the tooth becomes functional. We find that areas sustaining compression loads are pre-structured with sparse collagenous networks and large blood vessels, whereas other areas contain dense collagen networks with few blood vessels. Therefore, the PDL develops as a non-uniform structure, with an architecture designed to sustain specific types of load in different areas. Based on these findings, we propose that ligaments in general should be regarded as non-uniform entities structured for optimal functioning under variable load regimes.2019-09-26T00:00:00

Improved OCT Human Corneal segmentation Using Bayesian Residual Transform

The inherent poor signal to noise ratio of Optical Coherent Tomography(OCT) is considered as a main limitation of OCT segmentation,particularly because images are sampled quickly, at high resolutions,and in-vivo. Furthermore, speckle noise is generated bythe reflections of the OCT LASER limits the ability of automaticallysegmenting OCT images. This paper presents a novel method toautomatically segment human corneal OCT images. The proposedmethod uses Bayesian Residual Transform (BRT) to build a noiserobust external force map, that guides active contours model to thecorneal data in OCT images. Experimental results show that theproposed method outperforms the classical as well as the state-ofthe-art methods

Angle-closure assessment in anterior segment OCT images via deep learning

Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients’ eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be beneficial to clinicians who seek to better understand the progression of the spectrum of angle-closure disease types, so as to further assist the assessment and required treatment at different stages of angle-closure disease. To be more specific, we first use an image alignment method to generate sequences of AS-OCT images. The ACA region is then localized automatically by segmenting an important biomarker - the iris - as this is a primary structural cue in identifying angle-closure disease. Finally, the AS-OCT images acquired in both dark and bright illumination conditions are fed into our Multi-Sequence Deep Network (MSDN) architecture, in which a convolutional neural network (CNN) module is applied to extract feature representations, and a novel ConvLSTM-TC module is employed to study the spatial state of these representations. In addition, a novel time-weighted cross-entropy loss (TC) is proposed to optimize the output of the ConvLSTM, and the extracted features are further aggregated for the purposes of classification. The proposed method is evaluated across 66 eyes, which include 1584 AS-OCT sequences, and a total of 16,896 images. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy

Ocular rigidity : a previously unexplored risk factor in the pathophysiology of open-angle glaucoma : assessment using a novel OCT-based measurement method

Le glaucome est la première cause de cécité irréversible dans le monde. Bien que sa pathogenèse demeure encore nébuleuse, les propriétés biomécaniques de l’oeil sembleraient jouer un rôle important dans le développement et la progression de cette maladie. Il est stipulé que la rigidité oculaire (RO) est altérée au travers les divers stades de la maladie et qu’elle serait le facteur le plus influent sur la réponse du nerf optique aux variations de la pression intraoculaire (PIO) au sein du glaucome. Pour permettre l’investigation du rôle de la RO dans le glaucome primaire à angle ouvert (GPAO), la capacité de quantifier la RO in vivo par l’entremise d’une méthode fiable et non-invasive est essentielle. Une telle méthode n’est disponible que depuis 2015. Basée sur l'équation de Friedenwald, cette approche combine l'imagerie par tomographie par cohérence optique (TCO) et la segmentation choroïdienne automatisée afin de mesurer le changement de volume choroïdien pulsatile (ΔV), ainsi que la tonométrie dynamique de contour Pascal pour mesurer le changement de pression pulsatile correspondant. L’objectif de cette thèse est d’évaluer la validité de cette méthode, et d’en faire usage afin d’investiguer le rôle de la RO dans les maladies oculaires, particulièrement le GPAO. Plus spécifiquement, cette thèse vise à : 1) améliorer la méthode proposée et évaluer sa validité ainsi que sa répétabilité, 2) investiguer l’association entre la RO et le dommage neuro-rétinien chez les patients glaucomateux, et ceux atteints d’un syndrome de vasospasticité, 3) évaluer l’association entre la RO et les paramètres biomécaniques de la cornée, 4) évaluer l’association entre la RO et les pics de PIO survenant suite aux thérapies par injections intravitréennes (IIV), afin de les prédire et de les prévenir chez les patients à haut risque, et 5) confirmer que la RO est réduite dans les yeux myopes. D’abord, nous avons amélioré le modèle mathématique de l’oeil utilisé pour dériver ΔV en le rendant plus précis anatomiquement et en tenant compte de la choroïde périphérique. Nous avons démontré la validité et la bonne répétabilité de cette méthodologie. Puis, nous avons effectué la mesure des coefficients de RO sur un large éventail de sujets sains et glaucomateux en utilisant notre méthode non-invasive, et avons démontré, pour la première fois, qu'une RO basse est corrélée aux dommages glaucomateux. Les corrélations observées étaient comparables à celles obtenues avec des facteurs de risque reconnus tels que la PIO maximale. Une forte corrélation entre la RO et les dommages neuro-rétiniens a été observée chez les patients vasospastiques, mais pas chez ceux atteints d'une maladie vasculaire ischémique. Cela pourrait potentiellement indiquer une plus grande susceptibilité au glaucome due à la biomécanique oculaire chez les patients vasospastiques. Bien que les paramètres biomécaniques cornéens aient été largement adoptés dans la pratique clinique en tant que substitut pour la RO, propriété biomécanique globale de l'oeil, nous avons démontré une association limitée entre la RO et ces paramètres, offrant une nouvelle perspective sur la relation entre les propriétés biomécaniques cornéennes et globales de l’oeil. Seule une faible corrélation entre le facteur de résistance cornéenne et la RO demeure après ajustement pour les facteurs de confusion dans le groupe des patients glaucomateux. Ensuite, nous avons présenté un modèle pour prédire l'amplitude des pics de PIO après IIV à partir de la mesure non-invasive de la RO. Ceci est particulièrement utile pour les patients à haut risque atteints de maladies rétiniennes exsudatives et de glaucome qui nécessiteraient des IIV thérapeutiques, et pourrait permettre aux cliniciens d'ajuster ou de personnaliser le traitement pour éviter toute perte de vision additionnelle. Enfin, nous avons étudié les différences de RO entre les yeux myopes et les non-myopes en utilisant cette technique, et avons démontré une RO inférieure dans la myopie axiale, facteur de risque du GPAO. Dans l'ensemble, ces résultats contribuent à l’avancement des connaissances sur la physiopathologie du GPAO. Le développement de notre méthode permettra non seulement de mieux explorer le rôle de la RO dans les maladies oculaires, mais contribuera également à élucider les mécanismes et développer de nouveaux traitements ciblant la RO pour contrer la déficience visuelle liée à ces maladies.Glaucoma is the leading cause of irreversible blindness worldwide. While its pathogenesis is yet to be fully understood, the biomechanical properties of the eye are thought to be involved in the development and progression of this disease. Ocular rigidity (OR) is thought to be altered through disease processes and has been suggested to be the most influential factor on the optic nerve head’s response to variations in intraocular pressure (IOP) in glaucoma. To further investigate the role of OR in open-angle glaucoma (OAG) and other ocular diseases such as myopia, the ability to quantify OR in living human eyes using a reliable and non-invasive method is essential. Such a method has only become available in 2015. Based on the Friedenwald equation, the method uses time-lapse optical coherence tomography (OCT) imaging and automated choroidal segmentation to measure the pulsatile choroidal volume change (ΔV), and Pascal dynamic contour tonometry to measure the corresponding pulsatile pressure change. The purpose of this thesis work was to assess the validity of the methodology, then use it to investigate the role of OR in ocular diseases, particularly in OAG. More specifically, the objectives were: 1) To improve the extrapolation of ΔV and evaluate the method’s validity and repeatability, 2) To investigate the association between OR and neuro-retinal damage in glaucomatous patients, as well as those with concomitant vasospasticity, 3) To evaluate the association between OR and corneal biomechanical parameters, 4) To assess the association between OR and IOP spikes following therapeutic intravitreal injections (IVIs), to predict and prevent them in high-risk patients, and 5) To confirm that OR is lower in myopia. First, we improved the mathematical model of the eye used to derive ΔV by rendering it more anatomically accurate and accounting for the peripheral choroid. We also confirmed the validity and good repeatability of the method. We carried out the measurement of OR coefficients on a wide range of healthy and glaucomatous subjects using this non-invasive method, and were able to show, for the first time, that lower OR is correlated with more glaucomatous damage. The correlations observed were comparable to those obtained with recognized risk factors such as maximum IOP. A strong correlation between OR and neuro-retinal damage was found in patients with concurrent vasospastic syndrome, but not in those with ischemic vascular disease. This could perhaps indicate a greater susceptibility to glaucoma due to ocular biomechanics in vasospastic patients. While corneal biomechanical parameters have been widely adopted in clinical practice as surrogate measurements for the eye’s overall biomechanical properties represented by OR, we have shown a limited association between these parameters, bringing new insight unto the relationship between corneal and global biomechanical properties. Only a weak correlation between the corneal resistance factor and OR remained in glaucomatous eyes after adjusting for confounding factors. In addition, we presented a model to predict the magnitude of IOP spikes following IVIs from the non-invasive measurement of OR. This is particularly useful for high-risk patients with exudative retinal diseases and glaucoma that require therapeutic IVIs, and could provide the clinician an opportunity to adjust or customize treatment to prevent further vision loss. Finally, we investigated OR differences between non-myopic and myopic eyes using this technique, and demonstrated lower OR in axial myopia, a risk factor for OAG. Overall, these findings provide new insights unto the pathophysiology of glaucomatous optic neuropathy. The development of our method will permit further investigation of the role of OR in ocular diseases, contributing to elucidate mechanisms and provide novel management options to counter vision impairment caused by these diseases

Deep learning-based improvement for the outcomes of glaucoma clinical trials

Glaucoma is the leading cause of irreversible blindness worldwide. It is a progressive optic neuropathy in which retinal ganglion cell (RGC) axon loss, probably as a consequence of damage at the optic disc, causes a loss of vision, predominantly affecting the mid-peripheral visual field (VF). Glaucoma results in a decrease in vision-related quality of life and, therefore, early detection and evaluation of disease progression rates is crucial in order to assess the risk of functional impairment and to establish sound treatment strategies. The aim of my research is to improve glaucoma diagnosis by enhancing state of the art analyses of glaucoma clinical trial outcomes using advanced analytical methods. This knowledge would also help better design and analyse clinical trials, providing evidence for re-evaluating existing medications, facilitating diagnosis and suggesting novel disease management. To facilitate my objective methodology, this thesis provides the following contributions: (i) I developed deep learning-based super-resolution (SR) techniques for optical coherence tomography (OCT) image enhancement and demonstrated that using super-resolved images improves the statistical power of clinical trials, (ii) I developed a deep learning algorithm for segmentation of retinal OCT images, showing that the methodology consistently produces more accurate segmentations than state-of-the-art networks, (iii) I developed a deep learning framework for refining the relationship between structural and functional measurements and demonstrated that the mapping is significantly improved over previous techniques, iv) I developed a probabilistic method and demonstrated that glaucomatous disc haemorrhages are influenced by a possible systemic factor that makes both eyes bleed simultaneously. v) I recalculated VF slopes, using the retinal never fiber layer thickness (RNFLT) from the super-resolved OCT as a Bayesian prior and demonstrated that use of VF rates with the Bayesian prior as the outcome measure leads to a reduction in the sample size required to distinguish treatment arms in a clinical trial

New Mechatronic Systems for the Diagnosis and Treatment of Cancer

Both two dimensional (2D) and three dimensional (3D) imaging modalities are useful tools for viewing the internal anatomy. Three dimensional imaging techniques are required for accurate targeting of needles. This improves the efficiency and control over the intervention as the high temporal resolution of medical images can be used to validate the location of needle and target in real time. Relying on imaging alone, however, means the intervention is still operator dependent because of the difficulty of controlling the location of the needle within the image. The objective of this thesis is to improve the accuracy and repeatability of needle-based interventions over conventional techniques: both manual and automated techniques. This includes increasing the accuracy and repeatability of these procedures in order to minimize the invasiveness of the procedure. In this thesis, I propose that by combining the remote center of motion concept using spherical linkage components into a passive or semi-automated device, the physician will have a useful tracking and guidance system at their disposal in a package, which is less threatening than a robot to both the patient and physician. This design concept offers both the manipulative transparency of a freehand system, and tremor reduction through scaling currently offered in automated systems. In addressing each objective of this thesis, a number of novel mechanical designs incorporating an remote center of motion architecture with varying degrees of freedom have been presented. Each of these designs can be deployed in a variety of imaging modalities and clinical applications, ranging from preclinical to human interventions, with an accuracy of control in the millimeter to sub-millimeter range

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Development of High-speed Optical Coherence Tomography for Time-lapse Non-destructive Characterization of Samples

Optical coherence tomography (OCT) is an established optical imaging modality which can obtain label-free, non-destructive 3D images of samples with micron-scale resolution and millimeter penetration. OCT has been widely adopted for biomedical researches