Trustworthy Deep Learning for Medical Image Segmentation

Despite the recent success of deep learning methods at achieving new state-of-the-art accuracy for medical image segmentation, some major limitations are still restricting their deployment into clinics. One major limitation of deep learning-based segmentation methods is their lack of robustness to variability in the image acquisition protocol and in the imaged anatomy that were not represented or were underrepresented in the training dataset. This suggests adding new manually segmented images to the training dataset to better cover the image variability. However, in most cases, the manual segmentation of medical images requires highly skilled raters and is time-consuming, making this solution prohibitively expensive. Even when manually segmented images from different sources are available, they are rarely annotated for exactly the same regions of interest. This poses an additional challenge for current state-of-the-art deep learning segmentation methods that rely on supervised learning and therefore require all the regions of interest to be segmented for all the images to be used for training. This thesis introduces new mathematical and optimization methods to mitigate those limitations.Comment: PhD thesis successfully defended on 1st July 2022. Examiners: Prof Sotirios Tsaftaris and Dr Wenjia Ba

Automated segmentation and characterisation of white matter hyperintensities

Neuroimaging has enabled the observation of damage to the white matter that occurs frequently in elderly population and is depicted as hyperintensities in specific magnetic resonance images. Since the pathophysiology underlying the existence of these signal abnormalities and the association with clinical risk factors and outcome is still investigated, a robust and accurate quantification and characterisation of these observations is necessary. In this thesis, I developed a data-driven split and merge model selection framework that results in the joint modelling of normal appearing and outlier observations in a hierarchical Gaussian mixture model. The resulting model can then be used to segment white matter hyperintensities (WMH) in a post-processing step. The validity of the method in terms of robustness to data quality, acquisition protocol and preprocessing and its comparison to the state of the art is evaluated in both simulated and clinical settings. To further characterise the lesions, a subject-specific coordinate frame that divides the WM region according to the relative distance between the ventricular surface and the cortical sheet and to the lobar location is introduced. This coordinate frame is used for the comparison of lesion distributions in a population of twin pairs and for the prediction and standardisation of visual rating scales. Lastly the cross-sectional method is extended into a longitudinal framework, in which a Gaussian Mixture model built on an average image is used to constrain the representation of the individual time points. The method is validated through a purpose-build longitudinal lesion simulator and applied to the investigation of the relationship between APOE genetic status and lesion load progression

Advanced neuroimaging techniques to study the development of the cerebral cortex, subplate and thalamus in preterm infants at 3 Tesla

Preterm infants are at increased risk of neurodevelopmental delay, cognitive dysfunction, and behavioural disturbances. Recent studies of older preterm children with cognitive impairments implicate morphological and functional cortical abnormalities. However elucidation of the preterm cortical abnormalities has been challenging due to specific neonatal features. Using 3 Tesla neonatal MR images and Expectation Maximisation/Markov Random Field segmentation with incorporation of a novel knowledge based technique for removal of mislabelled partial volume voxels, neonatal 3D cortical extraction was possible from 25 to 48 weeks gestation. This enabled the study of the true cortical scaling exponent, cortical thickness, regional volumes and curvature measurements. It showed a relative excess of the cortical surface area for its volume which corresponded with a change in the intrinsic curvature and fissuration up to 36 weeks gestation, after which, the relative growth of the surface area and volume were proportional leading to dominant changes in the extrinsic curvature and cortical folding. Thus the curvature measurements showed an important mechanistic property of convolution. By term equivalent age, the cortex was thicker and there were changes in cortical curvature although there were no differences in the cortical surface area of preterm infants compared to term born controls. There were specific frontal and parietal deficits in the cortical volume. Diffusion MR showed that although the early cortical anisotropy diminished to noise levels by 35 weeks, the mean diffusivity reduced during the entire third trimester due to changes in the radial diffusivity. Regional variations in the mean diffusivity occurred during development with frontal abnormalities persisting at term equivalent age. Subplate and thalamic quantification showed important development features during the third trimester, however in the absence of overt lesions no associations with cortical measures were found. Thus this thesis provides interesting and novel insights into the macroscopic and microscopic development of the cortex.Imperial Users onl

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

Translating computational modelling tools for clinical practice in congenital heart disease

Increasingly large numbers of medical centres worldwide are equipped with the means to acquire 3D images of patients by utilising magnetic resonance (MR) or computed tomography (CT) scanners. The interpretation of patient 3D image data has significant implications on clinical decision-making and treatment planning. In their raw form, MR and CT images have become critical in routine practice. However, in congenital heart disease (CHD), lesions are often anatomically and physiologically complex. In many cases, 3D imaging alone can fail to provide conclusive information for the clinical team. In the past 20-30 years, several image-derived modelling applications have shown major advancements. Tools such as computational fluid dynamics (CFD) and virtual reality (VR) have successfully demonstrated valuable uses in the management of CHD. However, due to current software limitations, these applications have remained largely isolated to research settings, and have yet to become part of clinical practice. The overall aim of this project was to explore new routes for making conventional computational modelling software more accessible for CHD clinics. The first objective was to create an automatic and fast pipeline for performing vascular CFD simulations. By leveraging machine learning, a solution was built using synthetically generated aortic anatomies, and was seen to be able to predict 3D aortic pressure and velocity flow fields with comparable accuracy to conventional CFD. The second objective was to design a virtual reality (VR) application tailored for supporting the surgical planning and teaching of CHD. The solution was a Unity-based application which included numerous specialised tools, such as mesh-editing features and online networking for group learning. Overall, the outcomes of this ongoing project showed strong indications that the integration of VR and CFD into clinical settings is possible, and has potential for extending 3D imaging and supporting the diagnosis, management and teaching of CHD

Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

Évaluation de la biomécanique cardiovasculaire par élastographie ultrasonore non-invasive

L’élastographie est une technique d’imagerie qui vise à cartographier in vivo les propriétés mécaniques des tissus biologiques dans le but de fournir des informations diagnostiques additionnelles. Depuis son introduction en imagerie ultrasonore dans les années 1990, l’élastographie a trouvé de nombreuses applications. Cette modalité a notamment été utilisée pour l’étude du sein, du foie, de la prostate et des artères par imagerie ultrasonore, par résonance magnétique ou en tomographie par cohérence optique. Dans le contexte des maladies cardiovasculaires, cette modalité a un fort potentiel diagnostique puisque l’athérosclérose modifie la structure des tissus biologiques et leurs propriétés mécaniques bien avant l’apparition de tout symptôme. Quelle que soit la modalité d’imagerie utilisée, l’élastographie repose sur : l’excitation mécanique du tissu (statique ou dynamique), la mesure de déplacements et de déformations induites, et l’inversion qui permet de recouvrir les propriétés mécaniques des tissus sous-jacents. Cette thèse présente un ensemble de travaux d’élastographie dédiés à l’évaluation des tissus de l’appareil cardiovasculaire. Elle est scindée en deux parties. La première partie intitulée « Élastographie vasculaire » s’intéresse aux pathologies affectant les artères périphériques. La seconde, intitulée « Élastographie cardiaque », s’adresse aux pathologies du muscle cardiaque. Dans le contexte vasculaire, l’athérosclérose modifie la physiologie de la paroi artérielle et, de ce fait, ses propriétés biomécaniques. La première partie de cette thèse a pour objectif principal le développement d’un outil de segmentation et de caractérisation mécanique des composantes tissulaires (coeur lipidique, tissus fibreux et inclusions calciques) de la paroi artérielle, en imagerie ultrasonore non invasive, afin de prédire la vulnérabilité des plaques. Dans une première étude (Chapitre 5), nous présentons un nouvel estimateur de déformations, associé à de l’imagerie ultrarapide par ondes planes. Cette nouvelle méthode d’imagerie permet d’augmenter les performances de l’élastographie non invasive. Dans la continuité de cette étude, on propose une nouvelle méthode d’inversion mécanique dédiée à l’identification et à la quantification des propriétés mécaniques des tissus de la paroi (Chapitre 6). Ces deux méthodes sont validées in silico et in vitro sur des fantômes d’artères en polymère. Dans le contexte cardiaque, les ischémies et les infarctus causés par l’athérosclérose altèrent la contractilité du myocarde et, de ce fait, sa capacité à pomper le sang dans le corps (fonction myocardique). En échocardiographie conventionnelle, on évalue généralement la fonction myocardique en analysant la dynamique des mouvements ventriculaires (vitesses et déformations du myocarde). L’abscence de contraintes physiologiques agissant sur le myocarde (contrairement à la pression sanguine qui contraint la paroi vasculaire) ne permet pas de résoudre le problème inverse et de retrouver les propriétés mécaniques du tissu. Le terme d’élastographie fait donc ici référence à l’évaluation de la dynamique des mouvements et des déformations et non à l’évaluation des propriétés mécanique du tissu. La seconde partie de cette thèse a pour principal objectif le développement de nouveaux outils d’imagerie ultrarapide permettant une meilleure évaluation de la dynamique du myocarde. Dans une première étude (Chapitre 7), nous proposons une nouvelle approche d’échocardiographie ultrarapide et de haute résolution, par ondes divergentes, couplée à de l'imagerie Doppler tissulaire. Cette combinaison, validée in vitro et in vivo, permet d’optimiser le contraste des images mode B ainsi que l’estimation des vitesses Doppler tissulaires. Dans la continuité de cette première étude, nous proposons une nouvelle méthode d’imagerie des vecteurs de vitesses tissulaires (Chapitre 8). Cette approche, validée in vitro et in vivo, associe les informations de vitesses Doppler tissulaires et le mode B ultrarapide de l’étude précédente pour estimer l’ensemble du champ des vitesses 2D à l’intérieur du myocarde.Elastography is an imaging technique that aims to map the in vivo mechanical properties of biological tissues in order to provide additional diagnostic information. Since its introduction in ultrasound imaging in the 1990s, elastography has found many applications. This method has been used for the study of the breast, liver, prostate and arteries by ultrasound imaging, magnetic resonance imaging (MRI) or optical coherence tomography (OCT). In the context of cardiovascular diseases (CVD), this modality has a high diagnostic potential as atherosclerosis, a common pathology causing cardiovascular diseases, changes the structure of biological tissues and their mechanical properties well before any symptoms appear. Whatever the imaging modality, elastography is based on: the mechanical excitation of the tissue (static or dynamic), the measurement of induced displacements and strains, and the inverse problem allowing the quantification of the mechanical properties of underlying tissues. This thesis presents a series of works in elastography for the evaluation of cardiovascular tissues. It is divided into two parts. The first part, entitled « Vascular elastography » focuses on diseases affecting peripheral arteries. The second, entitled « Cardiac elastography » targets heart muscle pathologies. In the vascular context, atherosclerosis changes the physiology of the arterial wall and thereby its biomechanical properties. The main objective of the first part of this thesis is to develop a tool that enables the segmentation and the mechanical characterization of tissues (necrotic core, fibrous tissues and calcium inclusions) in the vascular wall of the peripheral arteries, to predict the vulnerability of plaques. In a first study (Chapter 5), we propose a new strain estimator, associated with ultrafast plane wave imaging. This new imaging technique can increase the performance of the noninvasive elastography. Building on this first study, we propose a new inverse problem method dedicated to the identification and quantification of the mechanical properties of the vascular wall tissues (Chapter 6). These two methods are validated in silico and in vitro on polymer phantom mimicking arteries. In the cardiac context, myocardial infarctions and ischemia caused by atherosclerosis alter myocardial contractility. In conventional echocardiography, the myocardial function is generally evaluated by analyzing the dynamics of ventricular motions (myocardial velocities and deformations). The abscence of physiological stress acting on the myocardium (as opposed to the blood pressure which acts the vascular wall) do not allow the solving the inverse problem and to find the mechanical properties of the fabric. Elastography thus here refers to the assessment of motion dynamics and deformations and not to the evaluation of mechanical properties of the tissue. The main objective of the second part of this thesis is to develop new ultrafast imaging tools for a better evaluation of the myocardial dynamics. In a first study (Chapter 7), we propose a new approach for ultrafast and high-resolution echocardiography using diverging waves and tissue Doppler. This combination, validated in vitro and in vivo, optimize the contrast in B-mode images and the estimation of myocardial velocities with tissue Doppler. Building on this study, we propose a new velocity vector imaging method (Chapter 8). This approach combines tissue Doppler and ultrafast B-mode of the previous study to estimate 2D velocity fields within the myocardium. This original method was validated in vitro and in vivo on six healthy volunteers