<p>  </p> <p>In this study, we present advancements in smart contact lenses, highlighting their potential as minimally or non-invasive diagnostic and drug delivery platforms. The eyes, rich in physiological and diagnostic data, make contact lens sensors an effective tool for disease diagnosis. These sensors, particularly smart contact lenses, can measure various biomolecules like glucose, urea, ascorbate, and electrolytes (Na+, K+, Cl-, HCO3-) in ocular fluids, along with physical biomarkers such as movement of the eye, intraocular pressure (IOP) and ocular surface temperature (OST).</p> <p>The study explores the use of continuous, non-invasive contact lens sensors in clinical or point-of-care settings. Although promising, their practical application is hindered by the developmental stage of the field. This thesis addresses these challenges by examining the integration of contact lens sensors, covering their working principle, fabrication, sensitivity, and readout mechanisms, with a focus on monitoring glaucoma and eye health conditions like dry eye syndrome and inflammation.</p> <p>Our design adapts these sensors to fit various corneal curvatures and thicknesses. The lenses can visually indicate IOP through microfluidic channels' mechanical deformation under ambulatory conditions. We also introduce a colorimetric hydrogel tear fluid sensor that detects pH, electrolytes, and ocular surface temperature, indicating conditions like dry eye disease and inflammation.</p> <p>The evaluation of these contact lens sensors includes in vivo/vitro biocompatibility, ex vivo functionality studies, and in vivo safety assessments. Our comprehensive analysis aims to enhance the practicality and effectiveness of smart contact lenses in ophthalmic diagnostics and therapeutics.</p>

<p>  </p> <p>Heart disease is the leading cause of death for individuals with Duchenne muscular dystrophy (DMD). DMD is a devastating and progressive neuromuscular disease with no known cure. This X-linked genetic disorder affects nearly 1 in 5000 boys and manifests as debilitating muscle weakness and progressive cardiomyopathy (CM). While CM in some individuals with DMD progresses rapidly and fatally in their teenage years, others can live relatively symptom-free into their thirties or forties. Early identification and treatment can improve quality and length of life, but currently, there are no standard imaging biomarkers that can detect early onset or rapidly progressing DMD CM. Addressing this gap, we describe here a novel cardiac image analysis paradigm using 4D cardiac magnetic resonance imaging (CMR) to map left-ventricular kinematics comprehensively in DMD CM. The primary goal of this dissertation work is to introduce novel imaging biomarkers and computational methods to enable earlier diagnosis and precise prognosis for cardiac function in DMD. Central to this goal, we identified myocardial strain biomarkers that predict the early onset and rapid progression of cardiac disease in vulnerable patients. These findings bridge clinical gaps and pave the way for multi-center studies to characterize DMD CM progression and assessment of individual patient risk profiles for improved treatment and outcomes in DMD.</p>

Computed tomography has been widely used in medical diagnosis to generate accurate images of the body's internal organs. However, cancer risk is associated with high X-ray dose CT scans, limiting its applicability in medical diagnosis and telemedicine applications. CT scans acquired at low X-ray dose generate low-quality images with noise and streaking artifacts. Therefore we develop a deep learning-based CT image enhancement algorithm for improving the quality of low-dose CT images. Our algorithm uses a convolution neural network called DenseNet and Deconvolution network (DDnet) to remove noise and artifacts from the input image. To evaluate its advantages in medical diagnosis, we use DDnet to enhance chest CT scans of COVID-19 patients. We show that image enhancement can improve the accuracy of COVID-19 diagnosis (~5% improvement), using a framework consisting of AI-based tools. For training and inference of the image enhancement AI model, we use heterogeneous computing platform for accelerating the execution and decreasing the turnaround time. Specifically, we use multiple GPUs in distributed setup to exploit batch-level parallelism during training. We achieve approximately 7x speedup with 8 GPUs running in parallel compared to training DDnet on a single GPU. For inference, we implement DDnet using OpenCL and evaluate its performance on multi-core CPU, many-core GPU, and FPGA. Our OpenCL implementation is at least 2x faster than analogous PyTorch implementation on each platform and achieves comparable performance between CPU and FPGA, while FPGA operated at a much lower frequency. / Master of Science / Computed tomography has been widely used in the medical diagnosis of diseases, such as cancer/tumor, viral pneumonia, and more recently, COVID-19. However, the risk of cancer associated with X-ray dose in CT scans limits the use of computed tomography in biomedical imaging. Therefore we develop a deep learning-based image enhancement algorithm that can be used with low X-ray dose computed tomography scanners to generate high-quality CT images. The algorithm uses a state-of-the-art convolution neural network for increased performance and computational efficiency. Further, we use image enhancement algorithm to develop a framework of AI-based tools to improve the accuracy of COVID-19 diagnosis. We test and validate the framework with clinical COVID-19 data. Our framework applies to the diagnosis of COVID-19 and its variants, and other diseases that can be diagnosed via computed tomography. We utilize high-performance computing techniques to reduce the execution time of training and testing AI models in our framework. We also evaluate the efficacy of training and inference of the neural network on heterogeneous computing platforms, including multi-core CPU, many-core GPU, and field-programmable gate arrays (FPGA), in terms of speed and power consumption.

Des colloïdes à base de silice et de polystyrène ont été synthétisés. Les particules d’oxyde ont d’abord été élaborées et modifiées en surface, puis ont servi de germes au cours d’une étape de polymérisation du styrène. Deux procédés de polymérisation en phase hétérogène ont été utilisés (émulsion ou dispersion) menant à des colloïdes aux morphologies originales et contrôlées. Une étude morphologique par tomographie électronique a permis de mieux comprendre les mécanismes de croissance et d’organisation des particules de latex autour des germes de silice. La synthèse de particules Janus pour l’imagerie biomédicale est aussi décrite. Ces particules de silice ont été modifiées en surface par un chromophore biphotonique et un agent de reconnaissance de certaines cellules tumorales. Des études spectroscopiques et des tests de cytotoxicité ont été entrepris. / Hybrid colloids based on silica and polystyrene have been synthesized. Oxide particles were first elaborated, surface modified, and then used as seed in a styrene polymerization step. Two heterogeneous polymerisation proceeds were employed (emulsion or dispersion) leading to colloids with original and controlled morphologies. A morphological study by electronic tomography enabled to better understand growth and organisation mechanisms of latexes around silica seeds. Janus particles synthesis for biomedical imaging is also described. Silica particles were surface modified with a biphotonic chromophore and a tumor cells targeting agent. Spectroscopic studies and cytotoxicity tests were investigated.

La segmentation de régions d'intérêt dans le cadre de l'analyse d'images médicales et biomédicales reste encore à ce jour un challenge en raison notamment de la variété des modalités d'acquisition et des caractéristiques associées (bruit par exemple).Dans ce contexte particulier, cet exposé présente une méthode de segmentation de type contour actif dont l ‘énergie associée à l'obtention de l'équation d'évolution s'appuie sur une mesure de similarité entre les densités de probabilités (en niveau de gris) des régions intérieure et extérieure au contour au cours du processus itératif de segmentation. En particulier, nous nous intéressons à la famille particulière des alpha-divergences. L'intérêt principal de cette méthode réside (i) dans la flexibilité des alpha-divergences dont la métrique intrinsèque peut être paramétrisée via la valeur du paramètre alpha et donc adaptée aux distributions statistiques des régions de l'image à segmenter ; et (ii) dans la capacité unificatrice de cette mesure statistique vis-à-vis des distances classiquement utilisées dans ce contexte (Kullback- Leibler, Hellinger...). Nous abordons l'étude de cette mesure statistique tout d'abord d'un point de vue supervisé pour lequel le processus itératif de segmentation se déduit de la minimisation de l'alpha-divergence (au sens variationnel) entre la densité de probabilité courante et une référence définie a priori. Puis nous nous intéressons au point de vue non supervisé qui permet de s'affranchir de l'étape de définition des références par le biais d'une maximisation de distance entre les densités de probabilités intérieure et extérieure au contour. Par ailleurs, nous proposons une démarche d'optimisation de l'évolution du paramètre alpha conjointe au processus de minimisation ou de maximisation de la divergence permettant d'adapter itérativement la divergence à la statistique des données considérées. Au niveau expérimental, nous proposons une étude comparée des différentes approches de segmentation : en premier lieu, sur des images synthétiques bruitées et texturées, puis, sur des images naturelles. Enfin, nous focalisons notre étude sur différentes applications issues des domaines biomédicaux (microscopie confocale cellulaire) et médicaux (radiographie X, IRM) dans le contexte de l'aide au diagnotic. Dans chacun des cas, une discussion sur l'apport des alpha-divergences est proposée. / In the particular field of Computer-Aided-Diagnosis, the segmentation of particular regions of interest corresponding usually to organs is still a challenging issue mainly because of the various existing for which the charateristics of acquisition are very different (corrupting noise for instance). In this context, this PhD work introduces an original histogram-based active contour segmentation using alpha-divergence family as similarity measure. The method keypoint are twofold: (i) the flexibility of alpha-divergences whose metric could be parametrized using alpha value can be adaptedto the statistical distribution of the different regions of the image and (ii) the ability of alpha-divergence ability to enbed standard distances like the Kullback-Leibler's divergence or the Hellinger's one makes these divergences an interesting unifying tool.In this document, first, we propose a supervised version of proposed approach:. In this particular case, the iterative process of segmentation comes from alpha-divergenceminimization between the current probability density function and a reference one which can be manually defined for instance. In a second part, we focus on the non-supervised version of the method inorder to be able.In that particular case, the alpha-divergence maximization between probabilitydensity functions of inner and outer regions defined by the active contour is maximized. In addition, we propose an optimization scheme of the alpha parameter jointly with the optimization of the divergence in order to adapt iteratively the divergence to the inner statistics of processed data. Furthermore, a comparative study is proposed between the different segmentation schemes : first, on synthetic images then, on natural images. Finally, we focus on different kinds of biomedical images (cellular confocal microscopy) and medical ones (X-ray) for computer-aided diagnosis.

L’imagerie TEP repose à l’heure actuelle sur des détecteurs à base de cristaux scintillants ou de semi-conducteurs. Le projet CaLIPSO propose de tirer parti à la fois de l’émission de lumière (par effet Cerenkov) et de l’ionisation du milieu de détection, pour réaliser un détecteur de résolution temporelle et spatiale améliorée. Le milieu de détection, le TriMéthylBismuth liquide à température ambiante, de par sa forte teneur en Bismuth, permet une détection par effet photoélectrique efficace. Cette étude a pour objectif de concevoir et d’optimiser le détecteur optique du projet CaLIPSO, afin de prouver le concept de la détection de photons de 511 keV par effet Cerenkov dans le TMBi, et de caractériser les performances d’un tel détecteur en termes de résolution temporelle et efficacité de détection. Un premier démonstrateur a validé le principe de détection reposant sur l’effet Cerenkov, bien que ses performances soient décevantes. C’est la raison pour laquelle nous avons entrepris un effort d’optimisation en simulation Monte Carlo dans Geant4, afin d’améliorer la collection de la lumière Cerenkov dans le détecteur, et donc son efficacité de détection et sa résolution temporelle. Avant, nous avons mesuré les propriétés optiques du TMBi (indice de réfraction, absorption et diffusion de la lumière), afin d’être capables de modéliser la propagation de la lumière Cerenkov dans le détecteur. Nous avons également optimisé par simulation Monte Carlo l’outil permettant la mesure de résolution en temps, un cristal scintillant de YAP:Ce couplé à un PMT. Cela a permis une mesure plus fine de la résolution temporelle du démonstrateur. A l’issue de ces travaux, nous avons construit un second démonstrateur optique. On mesure alors une efficacité de détection de l’ordre de 32% pour une résolution en temps de 660 ps (FWHM). L’efficacité mesurée prouve que le détecteur est pleinement efficace à détecter les conversions photoélectriques du photon de 511 keV (27% des photons incidents). Plusieurs optimisations technologiques sont proposées pour améliorer la résolution temporelle, et espérer à l’avenir une mesure du temps de vol des photons gamma. / PET detectors are usually based on scintillation crystals or semiconductor materials. The CaLIPSO project aims to build a PET detector working on the double detection of Cerenkov light and pair productions in a novel detection material called TriMethylBismuth. This would allow at the same time an enhanced time resolution (thanks to the Cerenkov signal) and a excellent spatial resolution (thanks to the ionization signal). Liquid TMBi (at room temperature), thanks to its good photo fraction (47%), allows a good detection efficiency, principally by photoelectric effect. In this context, this work aims to design and optimize an optical detector as a proof of concept for the Cerenkov detection of 511 keV gamma photons, and to measure the time resolution and detection efficiency of such a detector. The optical signal based on Cerenkov effect in TMBi has been observed on a first demonstrator, but its performances were clearly inappropriate. So we used a Monte Carlo simulation (Geant4) of the detector in order to model the relevant phenomena and to optimize de detection. It appeared that light collection efficiency in the detector was the most important parameter to optimize so as to improve time resolution and detection efficiency. Before that, we measured TMBi optical properties (refractive index, light absorption and diffusion), in order to model accurately the Cerenkov light propagation in the detector. The tool used for the time resolution measurement is a YAP: Ce scintillator coupled to a PMT. We also needed to optimize this tool in order to allow a more accurate measurement of the detector time resolution. At the end of this work, a second version of the optical demonstrator was built. We measured a detection efficiency of 32%, and a time resolution of 660 ps FWHM. The measured efficiency proved that our detector is fully efficient to detect the photoelectric conversions of the 511 keV photons (27% of the incident photons). Several technological optimizations are proposed to further improve the time resolution, in order to be able to measure the gamma photons’ time-of-flight in the future.

The ongoing merger of the digital and optical components of the modern microscope is creating opportunities for new measurement techniques, along with new challenges for optical modelling. This thesis investigates several such opportunities and challenges which are particularly relevant to biomedical imaging. Fourier optics is used throughout the thesis as the underlying conceptual model, with a particular emphasis on three--dimensional Fourier optics. A new challenge for optical modelling provided by digital microscopy is the relaxation of traditional symmetry constraints on optical design. An extension of optical transfer function theory to deal with arbitrary lens pupil functions is presented in this thesis. This is used to chart the 3D vectorial structure of the spatial frequency spectrum of the intensity in the focal region of a high aperture lens when illuminated by linearly polarised beam. Wavefront coding has been used successfully in paraxial imaging systems to extend the depth of field. This is achieved by controlling the pupil phase with a cubic phase mask, and thereby balancing optical behaviour with digital processing. In this thesis I present a high aperture vectorial model for focusing with a cubic phase mask, and compare it with results calculated using the paraxial approximation. The effect of a refractive index change is also explored. High aperture measurements of the point spread function are reported, along with experimental confirmation of high aperture extended depth of field imaging of a biological specimen. Differential interference contrast is a popular method for imaging phase changes in otherwise transparent biological specimens. In this thesis I report on a new isotropic algorithm for retrieving the phase from differential interference contrast images of the phase gradient, using phase shifting, two directions of shear, and non--iterative Fourier phase integration incorporating a modified spiral phase transform. This method does not assume that the specimen has a constant amplitude. A simulation is presented which demonstrates good agreement between the retrieved phase and the phase of the simulated object, with excellent immunity to imaging noise.

This work explores a novel vision model based coding approach to encode medical images at a perceptually lossless quality, within the framework of the JPEG 2000 coding engine. Perceptually lossless encoding offers the best of both worlds, delivering images free of visual distortions and at the same time providing significantly greater compression ratio gains over its information lossless counterparts. This is achieved through a visual pruning function, embedded with an advanced model of the human visual system to accurately identify and to efficiently remove visually irrelevant/insignificant information. In addition, it maintains bit-stream compliance with the JPEG 2000 coding framework and subsequently is compliant with the Digital Communications in Medicine standard (DICOM). Equally, the pruning function is applicable to other Discrete Wavelet Transform based image coders, e.g., The Set Partitioning in Hierarchical Trees. Further significant coding gains are ex ploited through an artificial edge segmentation algorithm and a novel arithmetic pruning algorithm. The coding effectiveness and qualitative consistency of the algorithm is evaluated through a double-blind subjective assessment with 31 medical experts, performed using a novel 2-staged forced choice assessment that was devised for medical experts, offering the benefits of greater robustness and accuracy in measuring subjective responses. The assessment showed that no differences of statistical significance were perceivable between the original images and the images encoded by the proposed coder.

The mouse pancreas is a mixed exocrine and endocrine glandconsisting of three lobular compartments: the splenic, duodenal and gastric lobes. During embryogenesis, the pancreas forms from two progenitor populations located on the dorsal and ventral side of the primitive gut tube. These anlagen are brought in close proximity as the gut elongates and rotates, and fuse to form a single organ. The splenic and duodenal lobes develop from the dorsal and ventral anlagen, respectively. In the adult pancreas, exocrine tissue secretes digestive enzymes intothe gut lumen to support nutrient uptake. The endocrine Islets of Langerhans are scattered throughout the exocrine tissue and aid in regulation of energy homeostasis through the secretion of hormones. One of the key players in energy homeostasis is the pancreatic ß-cell, which is the most abundant cell type of the islets. The β-cells regulates blood glucose levels through the action of insulin. Conditions where this regulation does not function properly are gathered under the common name of Diabetes mellitus. Type 1 diabetes (T1D) is characterized by insulin deficiency due to autoimmune destruction of the ß-cells. Using recently developed protocols for optical projection tomography (OPT) whole-organ imaging, we have revealed new spatial and quantitative aspects on ß-cell mass dynamics and immune infiltration during the course of T1D development in the non-obese diabetic (NOD) mouse model. We show that although immune infiltration appears to occur asynchronously throughout the organ, smaller islets, mainly located in the periphery of the organ, preferentially loose their ß-cells during early stages of disease progression. Larger islets appear more resistant to the autoimmune attack and our data indicate the existence of a compensatory proliferative capacity within these islets. We also report the appearance of structures resembling tertiary lymphoid organs (TLOs) in association with the remaining islets during later phases of T1D progression. OPT has already proven to be a useful tool for assessments of ß-cellmass in the adult mouse pancreas. However, as with other techniques, previous protocols have relied on a tedious degree of manual postivacquisition editing. To further refine OPT-based assessment of pancreatic ß-cell mass distribution in the murine pancreas, we implemented a computational statistical approach, Contrast-Limited Adaptive Histogram Normalisation (CLAHE), to the OPT projection data of pancreata from C57Bl/6 mice. This methodology provided increased islet detection sensitivity, improved islet morphology and diminished subjectivity in thresholding for reconstruction and quantification. Using this approach, we could report a substantially higher number of islets than previously described for this strain and provide evidence of significant differences in islet mass distribution between the pancreatic lobes. The gastric lobe stood out in particular and contained a 75% higher islet density as compared to the splenic lobe. Although the development of the early pancreatic buds has been relatively well studied, later morphogenetic events are less clear and information regarding the formation of the gastric lobe has largely been missing. Using OPT we have generated a quantitative three-dimensional road map of pancreatic morphogenesis in the mouse. We show that the gastric lobe forms as a perpendicular outgrowth fromthe stem of the dorsal pancreas at around embryonic day (e) 13.5, which grows into a mesenchymal domain overlaying the pyloric sphincter and proximal part of the glandular stomach. By analyzing mutant mice with aberrant spleen development, we further demonstrate that proper formation of the gastric lobe is dependent on the initial formation of the closely positioned spleen, indicating a close interplay between pancreatic and splenic mesenchyme during development. Additionally, we show that the expression profile of markers for pancreatic multipotent progenitors within the pancreas is heterogenous with regards to lobular origin. Altogether, our studies regarding the morphogenesis and adult constitution of the mouse pancreas recognize lobular heterogeneities that add important information for future interpretations of this organ.

Although the major functions of the lymphatic system are fairly well defined, its vasculature has yet to be well characterized in comparison to its blood vasculature counterpart. Recent advances in optical imaging techniques have allowed for more detailed and quantitative evaluations of lymph flow dynamics and mechanism. A rat tail is often used for investigations of lymph flow because of the simple geometry, superficial nature, and disease progression models of its collecting lymphatic vessels. In this study, a pressure cuff system was fabricated and coupled with an existing functional near infrared (NIR) imaging system to measure the overall pumping pressure of the lymphatic vessels of a rat tail. In addition to adapting the system for use on rodents, previous systems used for measuring lymphatic pumping pressure in humans were improved upon in several ways. The system defined here utilizes closed-loop feedback control of pressure application at smaller, more precise intervals. Using this device, a significant difference in lymphatic vessel pumping pressure was detected between a control case and a treatment case in which a vasoactive substance with a nitric oxide donor (GTNO ointment) was applied to the tail. Although it is known that nitric oxide plays a crucial physiologic role in propagation of flow through lymphatic vessels, this study has quantified its significant pharmacological reduction of pumping pressure for the first time.