Parmi les diverses modalités biométriques qui permettent l'identification des personnes, l'iris est considéré comme très fiable, avec un taux d'erreur remarquablement faible. Toutefois, ce niveau élevé de performances est obtenu en contrôlant la qualité des images acquises et en imposant de fortes contraintes à la personne (être statique et à proximité de la caméra). Cependant, dans de nombreuses applications de sécurité comme les contrôles d'accès, ces contraintes ne sont plus adaptées. Les images résultantes souffrent alors de diverses dégradations (manque de résolution, artefacts...) qui affectent négativement les taux de reconnaissance. Pour contourner ce problème, il est possible d’exploiter la redondance de l’information découlant de la disponibilité de plusieurs images du même œil dans la séquence enregistrée. Cette thèse se concentre sur la façon de fusionner ces informations, afin d'améliorer les performances. Dans la littérature, diverses méthodes de fusion ont été proposées. Cependant, elles s’accordent sur le fait que la qualité des images utilisées dans la fusion est un facteur crucial pour sa réussite. Plusieurs facteurs de qualité doivent être pris en considération et différentes méthodes ont été proposées pour les quantifier. Ces mesures de qualité sont généralement combinées pour obtenir une valeur unique et globale. Cependant, il n'existe pas de méthode de combinaison universelle et des connaissances a priori doivent être utilisées, ce qui rend le problème non trivial. Pour faire face à ces limites, nous proposons une nouvelle manière de mesurer et d'intégrer des mesures de qualité dans un schéma de fusion d'images, basé sur une approche de super-résolution. Cette stratégie permet de remédier à deux problèmes courants en reconnaissance par l'iris: le manque de résolution et la présence d’artefacts dans les images d'iris. La première partie de la thèse consiste en l’élaboration d’une mesure de qualité pertinente pour quantifier la qualité d’image d’iris. Elle repose sur une mesure statistique locale de la texture de l’iris grâce à un modèle de mélange de Gaussienne. L'intérêt de notre mesure est 1) sa simplicité, 2) son calcul ne nécessite pas d'identifier a priori les types de dégradations, 3) son unicité, évitant ainsi l’estimation de plusieurs facteurs de qualité et un schéma de combinaison associé et 4) sa capacité à prendre en compte la qualité intrinsèque des images mais aussi, et surtout, les défauts liés à une mauvaise segmentation de la zone d’iris. Dans la deuxième partie de la thèse, nous proposons de nouvelles approches de fusion basées sur des mesures de qualité. Tout d’abord, notre métrique est utilisée comme une mesure de qualité globale de deux façons différentes: 1) comme outil de sélection pour détecter les meilleures images de la séquence et 2) comme facteur de pondération au niveau pixel dans le schéma de super-résolution pour donner plus d'importance aux images de bonnes qualités. Puis, profitant du caractère local de notre mesure de qualité, nous proposons un schéma de fusion original basé sur une pondération locale au niveau pixel, permettant ainsi de prendre en compte le fait que les dégradations peuvent varier d’une sous partie à une autre. Ainsi, les zones de bonne qualité contribueront davantage à la reconstruction de l'image fusionnée que les zones présentant des artéfacts. Par conséquent, l'image résultante sera de meilleure qualité et pourra donc permettre d'assurer de meilleures performances en reconnaissance. L'efficacité des approches proposées est démontrée sur plusieurs bases de données couramment utilisées: MBGC, Casia-Iris-Thousand et QFIRE à trois distances différentes. Nous étudions séparément l'amélioration apportée par la super-résolution, la qualité globale, puis locale dans le processus de fusion. Les résultats montrent une amélioration importante apportée par l'utilisation de la qualité globale, amélioration qui est encore augmentée en utilisant la qualité locale / Among the large number of biometric modalities, iris is considered as a very reliable biometrics with a remarkably low error rate. The excellent performance of iris recognition systems are obtained by controlling the quality of the captured images and by imposing certain constraints on users, such as standing at a close fixed distance from the camera. However, in many real-world applications such as control access and airport boarding these constraints are no longer suitable. In such non ideal conditions, the resulting iris images suffer from diverse degradations which have a negative impact on the recognition rate. One way to try to circumvent this bad situation is to use some redundancy arising from the availability of several images of the same eye in the recorded sequence. Therefore, this thesis focuses on how to fuse the information available in the sequence in order to improve the performance. In the literature, diverse schemes of fusion have been proposed. However, they agree on the fact that the quality of the used images in the fusion process is an important factor for its success in increasing the recognition rate. Therefore, researchers concentrated their efforts in the estimation of image quality to weight each image in the fusion process according to its quality. There are various iris quality factors to be considered and diverse methods have been proposed for quantifying these criteria. These quality measures are generally combined to one unique value: a global quality. However, there is no universal combination scheme to do so and some a priori knowledge has to be inserted, which is not a trivial task. To deal with these drawbacks, in this thesis we propose of a novel way of measuring and integrating quality measures in a super-resolution approach, aiming at improving the performance. This strategy can handle two types of issues for iris recognition: the lack of resolution and the presence of various artifacts in the captured iris images. The first part of the doctoral work consists in elaborating a relevant quality metric able to quantify locally the quality of the iris images. Our measure relies on a Gaussian Mixture Model estimation of clean iris texture distribution. The interest of our quality measure is 1) its simplicity, 2) its computation does not require identifying in advance the type of degradations that can occur in the iris image, 3) its uniqueness, avoiding thus the computation of several quality metrics and associated combination rule and 4) its ability to measure the intrinsic quality and to specially detect segmentation errors. In the second part of the thesis, we propose two novel quality-based fusion schemes. Firstly, we suggest using our quality metric as a global measure in the fusion process in two ways: as a selection tool for detecting the best images and as a weighting factor at the pixel-level in the super-resolution scheme. In the last case, the contribution of each image of the sequence in final fused image will only depend on its overall quality. Secondly, taking advantage of the localness of our quality measure, we propose an original fusion scheme based on a local weighting at the pixel-level, allowing us to take into account the fact that degradations can be different in diverse parts of the iris image. This means that regions free from occlusions will contribute more in the image reconstruction than regions with artefacts. Thus, the quality of the fused image will be optimized in order to improve the performance. The effectiveness of the proposed approaches is shown on several databases commonly used: MBGC, Casia-Iris-Thousand and QFIRE at three different distances: 5, 7 and 11 feet. We separately investigate the improvement brought by the super-resolution, the global quality and the local quality in the fusion process. In particular, the results show the important improvement brought by the use of the global quality, improvement that is even increased using the local quality

Correlative light and electron microscopy (CLEM) is a powerful approach to investigate the molecular ultrastructure of labeled cell compartments. However, quantitative CLEM studies are rare, mainly due to small sample sizes and the sensitivity of fluorescent proteins to strong fixatives and contrasting reagents for EM. Here, we show that fusion of a self-labeling protein to insulin allows for the quantification of age-distinct insulin granule pools in pancreatic beta cells by a combination of super resolution and transmission electron microscopy on Tokuyasu cryosections. In contrast to fluorescent proteins like GFP organic dyes covalently bound to self-labeling proteins retain their fluorescence also in epoxy resin following high pressure freezing and freeze substitution, or remarkably even after strong chemical fixation. This enables for the assessment of age-defined granule morphology and degradation. Finally, we demonstrate that this CLEM protocol is highly versatile, being suitable for single and dual fluorescent labeling and detection of different proteins with optimal ultrastructure preservation and contrast.

Diplomová práca je zameraná na spracovávanie obrazov sietnice s vysokým rozlíšením. Cieľom práce je zlepšiť výslednú kvalitu výsledných snímkov sietnice získaných zo sekvencie snímkov nižšej kvality. Jednotlivé snímky sú najskôr spracované pomocou bilaterálnej filtrácie a zlepšenia kontrastu. v ďalšom kroku sú odstránené rozmazané snímky a snímky zobrazujúce iné časti sietnice. Posun medzi jednotlivými snímkami v sekvencii sa odhaduje pomocou fázovej korelácie, a tieto obrazy sú potom fúzované do výsledného snímku s vysokým rozlíšením pomocou priemerovania a využitia superrozlišovacej techniky, presnejšie regularizácie pomocou bilaterálneho celkového rozptylu. Výsledné mediánové hodnoty skóre kvality získaných obrazov sú PIQUE 0.2600, NIQE 0.0701, a BRISQUE 0.3936 pre techniku priemerovania, a PIQUE 0.1063, NIQE 0.0507, and BRISQUE 0.1570 pre superrozlišovaciu techniku.

Les épines dendritiques sont de petites protrusions post-synaptiques présentant des changements morphologiques corrélés avec la plasticité synaptique. Elles ont pour origine les filopodes dendritiques qui s’élargissent lors du contact avec l’axone. Ces changements morphologiques impliquent une grande variété de molécules dont des protéines associées à l’actine et des protéines d’adhésion. Cependant, comment ces différentes protéines sont coordonnées dans le temps et l’espace est encore largement méconnu. De plus, les techniques de microscopie conventionnelle ne permettent pas d’étudier l’organisation et la dynamique de ces protéines dans les épines dont la taille est proche de la limite de resolution. L’objectif de ma thèse a donc été d’explorer le rôle des protéines associées à l’actine ainsi que celui des protéines d’adhésion N-cadhérines dans l’organisation et la dynamique du cytosquelette d’actine des épines dendritiques. Dans une première étude, nous avons suivi la motilité des filopodes et épines dendritiques de neurones en visualisant l’actine-GFP. Nous avons couplé cette approche avec : 1) une technique de piégeage optique de microsphères recouvertes de N-cadhérines ou des substrats micro-imprimés également recouverts de N-cadhérines afin de contrôler temporellement et spatialement les adhésions cadhérine-cadhérine, 2) la stimulation pharmacologique de la myosine II afin d’induire la contraction F-actine/myosine et 3) l’expression de mutants de N-cadhérine non adhésifs. Nous avons ainsi démontré que la stabilisation des filopodes en épines était dépendante de l’engagement d’un embrayage moléculaire entre les adhésions trans-synaptiques N-cadhérine et le flux rétrograde d’actine généré par les myosines II. Dans une deuxième étude, nous avons utilisé la microscopie super-résolutive (PALM et dSTORM) et le suivi de protéines individuelles (sptPALM) pour étudier l’organisation et la dynamique à l’échelle nanométrique des protéines à l’origine des réseaux d’actine branchés dans les épines. Ainsi, nous avons caractérisé la localisation et la dynamique de l’actine, du complexe Arp2/3, du complexe WAVE, d’IRSp53, de VASP et de Rac-1. Nous avons montré que, contrairement aux structures motiles classiques comme lamellipode, le réseau d’actine branché dans les épines n’ést pas formé aux extrémités protrusives puis incorporé dans un flux rétrograde d’actine. Ce réseau est initié à la PSD puis croît vers l’extérieur afin de générer les protrusions membranaires responsablent des changements morphologiques de l’épine. Nos résultats montrent également qu’un contrôle strict de l’activité de Rac-1 est nécessaire au maintien de la morphologie des épines dendritiques et de l’architecture du réseau d’actine branché. L’ensemble de mon travail souligne l’importance du rôle de l’organisation à l’échelle nanométrique du réseau d’actine branché et des adhésions N-cadhérine dans la dynamique et la formation des épines dendritiques. Ces résultats pourraient avoir un rôle important dans la compréhension des changements morphologiques lors de la plasticité synaptique. / Dendritic spines are tiny post-synaptic protrusions exhibiting changes in morphology correlated with synaptic plasticity. They originate from motile dendritic filopodia, which enlarge after contacting axons. These morphological changes involve a wide number of molecular actors, including actin-binding proteins, and adhesion molecules. However, how these various molecular components are coordinated temporally and spatially to tune changes in spine shape remains unclear. Furthermore, conventional photonic microscopy techniques could not achieved the spatial resolution required to study the dynamic nanoscale organization of these proteins within the micron size dendritic spines. The objective of my Ph.D. was to unravel how actin-binding proteins and N-cadherin adhesion regulate the organization and dynamics of F-actin network in dendritic spines. In a first study, we measured the motility of dendritic filopodia and spines by time lapse imaging of actin-GFP in primary hippocampal neurons. We combined those measurements with: 1) manipulation of N-cadherin coated beads with optical tweezers, or micropatterns to control the timing and location of nascent N-cadherin adhesions, 2) pharmacological stimulation of myosin II to trigger contraction of the F-actin/myosin network and 3) expression of non-adhesive N-cadherin mutants to compete for the interaction between N-cadherin adhesion and F-actin. Using these different approaches we demonstrated that the stabilization of dendritic filopodia into mature spines was dependent on the engagement of a molecular clutch between trans-synaptic N-cadherin adhesions and the myosin driven F-actin flow. In a second study, we used super resolution microscopy (PALM and dSTORM) and single protein tracking (sptPALM) to study the dynamic nanoscale organizations of branched actin networks within dendritic spines. Using these technics, we characterized within dendritic spines, the localization and dynamics of actin, Arp2/3 complex, WAVE complex, IRSp53, VASP and Rac-1. We established that, opposite to classical motile structures such as the lamellipodium, branched F-actin networks in dendritic spines are not formed at the tip of membrane protrusions and incorporated in a retrograde flow. On the contrary, they are growing outwards from the PSD generating membrane protrusions responsible for spine motility. We also show that a thigh control of Rac1 activity is required to maintain dendritic spine morphology and branched actin network organization. Altogether, these studies point out the role of the nanoscale functional organization of F-actin networks and its linkage to adhesion proteins in the regulation of dendritic spine formation and dynamics. These findings may have important implications in the understanding of spine morphology changes driven by synaptic activity.

<p> The urgency for deeper knowledge about nervous system function and dysfunction has never been greater. With increasing rates of mental disorders and expanding healthcare costs, deciphering details of axonal development is essential to meeting this imperative. Models of neuronal growth are improving as roles of microtubules and motor proteins surface. However, traditional motor protein studies focus on intracellular cargo transport, leaving deficits in knowledge about how these proteins organize cytoskeletal filaments in the axon and growth cone during neuronal development. Inconsistent findings on microtubule activity in growing axons also leave gaps in quantitative assessments of microtubule translocation and assembly, limiting the ability to construct a comprehensive model of axonal elongation.</p> The goal of this study was to provide a more complete neuronal growth cone model by determining how individual microtubule translocation and assembly, mass microtubule movements, and motor protein activity contribute to axonal elongation. The underlying mechanisms of these processes were investigated by testing the roles of dynein and microtubule dynamics in axonal elongation of <i>Aplysia </i><i>californica </i>neurons using transillumination, fluorescent speckle, and super-resolution microscopy. Pharmacologically inhibiting either dynein activity or microtubule assembly reduced both bulk and individual microtubule anterograde translocation and neurite elongation rates. Suppressing both processes simultaneously had compensatory rather than additive effects. Super-resolution imaging also revealed fewer dynein motors co-localized with microtubules when microtubule assembly was inhibited. These results strongly suggest that disrupting microtubule assembly blocks neurite outgrowth partly because it inhibits dynein-mediated bulk microtubule translocation.

Computational methods in single molecule localization microscopy Abstract Fluorescence microscopy is one of the chief tools used in biomedical research as it is a non invasive, non destructive, and highly specific imaging method. Unfortunately, an optical microscope is a diffraction limited system. Maximum achievable spatial resolution is approximately 250 nm laterally and 500 nm axially. Since most of the structures in cells researchers are interested in are smaller than that, increasing resolution is of prime importance. In recent years, several methods for imaging beyond the diffraction barrier have been developed. One of them is single molecule localization microscopy, a powerful method reported to resolve details as small as 5 nm. This approach to fluorescence microscopy is very computationally intensive. Developing methods to analyze single molecule data and to obtain super-resolution images are the topics of this thesis. In localization microscopy, a super-resolution image is reconstructed from a long sequence of conventional images of sparsely distributed single photoswitchable molecules that need to be sys- tematically localized with sub-diffraction precision. We designed, implemented, and experimentally verified a set of methods for automated processing, analysis and visualization of data acquired...

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The accurate assessment of hemodynamics and its parameters play an important role when diagnosing cardiovascular diseases. In this context, 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique that facilitates hemodynamic parameter assessment as well as quantitative and qualitative analysis of three-directional flow over time. However, the assessment is limited by noise, low spatio-temporal resolution and long acquisition times. Consequently, in regions characterized by transient, rapid flow dynamics, such as the aorta and heart, capturing these rapid transient flows remains particularly challenging. Recent research has shown the feasibility of machine learning models to effectively denoise and increase the spatio-temporal resolution of 4D Flow MRI. However, temporal super-resolution networks, which can generalize on unseen domains and are independent on boundary segmentations, remain unexplored.  This study aims to investigate the feasibility of a neural network for temporal super-resolution and denoising of 4D Flow MRI data. To achieve this, we propose a residual convolutional neural network (based on the 4DFlowNet from Ferdian et al.) providing an end-to-end mapping from temporal low resolution space to high resolution space. The network is trained on patient-specific cardiac models created with computational-fluid dynamic (CFD) simulations covering a full cardiac cycle. For clinical contextualization, performance is assessed on clinical patient data. The study shows the potential of the 4DFlowNet for temporal-super resolution with an average relative error of 16.6 % on an unseen cardiac domain, outperforming deterministic methods such as linear and cubic interpolation. We find that the network effectively reduces noise and recovers high-transient flow by a factor of 2 on both in-silico and in-vivo cardiac datasets. The prediction results in a temporal resolution of 20 ms, going beyond the general clinical routine of 30-40 ms. This study exemplifies the performance of a residual CNN for temporal super-resolution of 4D flow MRI data, providing an option to extend evaluations to aortic geometries and to further develop different upsampling factors and temporal resolutions. / En noggrann bedömning av hemodynamiken och dess parametrar spelar en viktig roll vid diagnos av kardiovaskulära sjukdomar. I detta sammanhang är 4D Flow Magnetic Resonance Imaging (4D Flow MRI) en icke-invasiv mätteknik som underlättar bedömning av hemodynamiska parametrar samt kvantitativ och kvalitativ analys av flöde. Bedömningen begränsas av brus, låg spatio-temporal upplösning och långa insamlingstider. I områden som karakteriseras av snabb flödesdynamik, såsom aorta och hjärta, är det därför fortfarande särskilt svårt att fånga dessa snabba transienta flöden. Ny forskning har visat att det är möjligt att använda maskininlärningsmodeller för att effektivt reducera brus och öka den spatio-temporala upplösningen i 4D Flow MRI. Nätverk för temporal superupplösning, som kan generaliseras till osedda domäner och är oberoende av segmentering, är fortfarande outforskade.  Denna studie syftar till att undersöka genomförbarheten av ett neuralt nätverk för temporal superupplösning och brusreducering av 4D Flow MRI-data. För att uppnå detta föreslår vi ett residual faltningsneuralt nätverk (baserat på 4DFlowNet från Ferdian et al.) som tillhandahåller en end-to-end-mappning från temporalt lågupplöst utrymme till högupplöst utrymme. Nätverket tränas på patientspecifika hjärtmodeller som skapats med CFD-simuleringar som spänner över en hel hjärtcykel. För klinisk kontextualisering utvärderas nätverkets prestanda på kliniska patientdata. Studien visar potentialen av 4DFlowNet för temporal superupplösning med ett genomsnittligt relativt fel på 16,6 % på en osedd hjärtdomän, vilket överträffar deterministiska metoder som linjär och kubisk interpolation. Vi konstaterar att nätverket effektivt minskar brus och återställer högtransient flöde med en faktor på 2 på både in-silico ochin-vivo hjärtdataset. Förutsägelsen resulterar i en temporal upplösning på 20 ms, vilket är mer än den allmänna kliniska rutinen på 30-40 ms. Denna studie exemplifierar prestandan hos en residual CNN för temporal superupplösning av 4D-flödes-MRI-data, vilket ger möjlighet att utvidga utvärderingarna till aortageometrier och att vidareutveckla olika uppsamplingsfaktorer och temporala upplösningar.

In synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), an imaging radar emits electromagnetic waves of varying frequencies towards a target and the backscattered waves are collected. By either moving the radar antenna or rotating the target and combining the collected waves, a much longer synthetic aperture can be created. These radar measurements can be used to determine the radar cross-section (RCS) of the target and to reconstruct an estimate of the target. However, the reconstructed images will suffer from spectral leakage effects and are limited in resolution. Many methods of enhancing the images exist and some are based on deep learning. Most commonly the deep learning methods rely on high-resolution ground truth data of the scene to train a neural network to enhance the radar images. In this thesis, a method that does not rely on any high-resolution ground truth data is applied to train a convolutional neural network to enhance radar images. The network takes a conventional ISAR image subject to spectral leakage effects as input and outputs an enhanced ISAR image which contains much more defined features. New RCS measurements are created from the enhanced ISAR image and the network is trained to minimise the difference between the original RCS measurements and the new RCS measurements. A sparsity constraint is added to ensure that the proposed enhanced ISAR image is sparse. The synthetic training data consists of scenes containing point scatterers that are either individual or grouped together to form shapes. The scenes are used to create synthetic radar measurements which are then used to reconstruct ISAR images of the scenes. The network is tested using both synthetic data and measurement data from a cylinder and two aeroplane models. The network manages to minimise spectral leakage and increase the resolution of the ISAR images created from both synthetic and measured RCSs, especially on measured data from target models which have similar features to the synthetic training data.  The contributions of this thesis work are firstly a convolutional neural network that enhances ISAR images affected by spectral leakage. The neural network handles complex-valued signals as a single channel and does not perform any rescaling of the input. Secondly, it is shown that it is sufficient to calculate the new RCS for much fewer frequency samples and angular positions and compare those measurements to the corresponding frequency samples and angular positions in the original RCS to train the neural network.