Cell transplantation and stem cell therapy are promising approaches for regenerative medicine and are of interest to researchers and clinicians worldwide. However, currently, no imaging technique that allows three-dimensional in vivo inspection of therapeutically administered cells in host tissues is available. Therefore, we investigate magnetomotive optical coherence tomography (MM-OCT) of cells labeled with magnetic particles as a potential noninvasive cell tracking method. We develop magnetomotive imaging of mesenchymal stem cells for future cell therapy monitoring. Cells were labeled with fluorescent iron oxide nanoparticles, embedded in tissue-mimicking agar scaffolds, and imaged using a microscope setup with an integrated MM-OCT probe. Magnetic particle-induced motion in response to a pulsed magnetic field of 0.2 T was successfully detected by OCT speckle variance analysis, and cross-sectional and volumetric OCT scans with highlighted labeled cells were obtained. In parallel, fluorescence microscopy and laser speckle reflectometry were applied as two-dimensional reference modalities to image particle distribution and magnetically induced motion inside the sample, respectively. All three optical imaging modalities were in good agreement with each other. Thus, magnetomotive imaging using iron oxide nanoparticles as cellular contrast agents is a potential technique for enhanced visualization of selected cells in OCT.

Intravital microscopy (IVM) is a well-established imaging technique for real-time monitoring of microscale lung tissue dynamics. Although accepted as a gold standard in respiratory research, its characteristic image features are scarcely understood, especially when trying to determine the actual position of alveolar walls. To allow correct interpretation of these images with respect to the true geometry of the lung parenchyma, we analyzed IVM data of alveoli in a mouse model in comparison with simultaneously acquired optical coherence tomography images. Several IVM characteristics, such as double ring structures or disappearing alveoli in regions of liquid filling, could be identified and related to the position of alveoli relative to each other. Utilizing a ray tracing approach based on an idealized geometry of the mouse lung parenchyma, two major reflection processes could be attributed to the IVM image formation: partial reflection and total internal reflection between adjacent alveoli. Considering the origin of the reflexes, a model was developed to determine the true position of alveolar walls within IVM images. These results allow thorough understanding of IVM data and may serve as a basis for the correction of alveolar sizes for more accurate quantitative analysis within future studies of lung tissue dynamics.

Since optical coherence tomography (OCT) provides three-dimensional high-resolution images of biological tissue, the benefit of polarization contrast in the field of dentistry is highlighted in this study. Polarization-sensitive OCT (PS OCT) with phase-sensitive recording is used for imaging dental and mucosal tissues in the human oral cavity in vivo. An enhanced polarization contrast of oral structures is reached by analyzing the signals of the co- and crosspolarized channels of the swept source PS OCT system quantitatively with respect to reflectivity, retardation, optic axis orientation, and depolarization. The calculation of these polarization parameters enables a high tissue-specific contrast imaging for the detailed physical interpretation of human oral hard and soft tissues. For the proof-of-principle, imaging of composite restorations and mineralization defects at premolars as well as gingival, lingual, and labial oral mucosa was performed in vivo within the anterior oral cavity. The achieved contrast-enhanced results of the investigated human oral tissues by means of polarizationsensitive imaging are evaluated by the comparison with conventional intensity-based OCT.

Tesis por compendio / [ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT. / [CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT. / [EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data. / García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400 / TESIS / Compendio

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Complex functions of the basal ganglia are affected by numerous sensory and sensitive stimuli. In our studies, we investigated parameters of sense of smell and vision in neurodegenerative diseases of the basal ganglia - Parkinson's disease (PD) and Huntington's disease (HD). In the first study, we use Odourized Markers Test (OMT) to determine its applicability in PD patients, and to determine whether it distinguishes olfactory disorders between neurodegenerative and other disorders. Results show that OMT is applicable for PD patients and comparable to Sniffin' Sticks as it demonstrates gains of lower scores in PD patients compared to healthy subjects, but they do not differentiate other etiology of olfactory disorders. In the next study, we tested the pleasantness of odor stimulants in PD patients using New test of odor pleasantness (NTOP). We investigated suitability and validity of its use. We found that PD patients had lower odor rating score compared to healthy group correlated with Sniffin' Sticks and OMT. In the following study, we examined whether PD patients with visual hallucinations (PDH+) have structural retinal changes measured by optical coherence tomography (OCT) and functional retinal changes examined by 2,5% contrast sensitivity test compared to PD patients without hallucinations...

La rétinopexie transsclérale est une technique de laser non invasive, visant à créer des ancrages de la rétine dans la choroïde, par la formation de multiples lésions de photocoagulation correspondant à des brulures focales reconnues comme cicatrices coagulatives atrophiques. Cette technique est utilisée chez l’humain, surtout chez l’enfant, en prévention d’un décollement de rétine. Malgré la procédure de laser, un re-décollement de la neurorétine est toujours possible. Cette technique est utilisée chez le chien, mais son application pourrait être utilisée sur un éventail d’espèces plus large, si un suivi des lésions de photocoagulation pouvait être effectué dans le temps afin de s’assurer de la conformité des cicatrices choriorétiniennes. La tomographie par cohérence optique (OCT), grâce à son grand pouvoir de résolution, de l’ordre de microns, pourrait être un outil efficace permettant de vérifier la structure des lésions de photocoagulation in vivo. À cet effet, notre étude a évalué la corrélation entre les coupes sagittales de lésion de photocoagulation, par histologie et OCT, en utilisant le lapin comme modèle animal. Notre étude a illustré la corrélation entre les images de photocoagulation à l’histologie et à l’OCT aux jours (J) 1, 7, 21 et 42 après le traitement laser. La diminution graduelle de la neurorétine, avec un aspect atrophique apparent dès J7, était visible dans les images d’histologie et d’OCT. L’hyperpigmentation histologique avait une claire correspondance avec l’hyper-brillance de la coupe OCT à J42. L’OCT semble être un outil précis et efficace pour le suivi d’une cicatrice choriorétinienne, effectuée au laser diode 810 nm. / Trans-scleral retinopexy is a non-invasive laser technique, aiming at anchoring the retina to the choroid, by producing multiple photocoagulation lesions corresponding to focal burns, named atrophic coagulating scars. This technique is used in humans, especially in children, to prevent retinal detachment. Despite the laser procedure, a re-detachment of the neuroretina is still possible. This technique is used mostly in dogs, but its application could be extended to other species, if a follow-up of the lesions could be carried out over time to ensure the conformity of the chorioretinal scars. Optical coherence tomography (OCT), thanks to its very high-resolution power, in the order of microns, could be an effective tool to evaluate photocoagulation lesions in vivo. For this purpose, a correlation between sagittal sections of photocoagulation lesions by histology and OCT was performed, using the rabbit as an animal model. Our study illustrated a comparison between histology and OCT photocoagulation images at days (D) 1, 7, 21 and 42 after laser treatment. The gradual decrease in neuroretinal thickness, with an atrophic appearance, was present as early as D7, and visible in both histology and OCT. The histological hyperpigmentation clearly corresponded to the hyper-brilliance of the OCT images at D42. The OCT appears to be a precise and effective tool for the follow-up over time of a chorioretinal scar, performed with an 810 nm diode laser.

[pt] O bunching de fótons é um dos mais celebrados efeitos de interferência de dois fóotons, associado à tendéncia de fótons indistinguíveis de tomarem o mesmo caminho quando há uma superposição dos pacotes de onda em um combinador de feixes óptico simétrico. Nós exploramos o fenômeno de interferência de dois fótons e mostramos que: a característica espectral de uma fonte de luz pode ser determinar através da técnica de Espectroscopia de Transformada de Fourier de Poucos Fótons de alta resolução que se mostra como uma técnica útil para a caracterização espectral de fontes ópticas débeis abaixo do limite coberto por técnicas clássicas de batimento heteródino; uma fonte de fótons com estatística sub-Poisson, a Fonte de Fótons Anunciadas com Óptica Linear, pode ser construída baseada apenas em óptica linear e estados coerentes atenuados uma vez que os anúncios sejam sintonizados nos picos de coincidência de um interferograma de Hong-Ou-Mandei quando o interferômetro é alimentado com estados em frequências diferentes; uma modificação do interferômetro de Hong-Ou-Mandel produz um aumento no número de coincidências, ao invés de sua diminuição, quando os pacotes de onda estão perfeitamente superpostos no interferômetro e o anúncio de fótons sintonizados nesse pico gera um feixe com distribuição sub-Poisson. A descrição matemática de cada experimento é detalhada e uma revisão extensa das ferramentas teóricas e práticas necessárias para o entendimento dos resultados é apresentada. / [en] Photon bunching is one of the most celebrated effects of two-photon interference, related to the tendency of indistinguishable photons to take the same path when there is a wave-packet overlapping in a symmetric beam splitter. Photon antibunching, the counterpart of photon bunching is, on turn, a desired effect in many applications such as single-photon generation. We explore the two-photon interference phenomena and show that: the spectral characteristics of a light source can be determined with a high resolution Few-Photon Fourier Transform Spectroscopy which proves to be a useful asset for spectral characterization of faint optical sources below the range covered by classical heterodyne beating techniques; a sub-Poisson photon source, the Linear-Optic Heralded Photon Source, can be constructed based only on linear optics and weak coherent states by time-tuning a Hong-Ou-Mandel interferometer fed with frequency-displaced coherent states and yields a second-order correlation function at zero time below one; a modified version a the Hong-Ou-Mandel interferometer produces a peak of coincidences instead of dip when the wave-packets are perfectly overlapped and the announcement of photons time-tuned to this coincidence peak yield an antibunched photon stream. The mathematical description of each experiment is detailed and an extensive review of the most important theoretical and practical tools for understanding of the results is presented.

Optical Coherence Tomography (OCT) has been around for more than 30 years and is still being continuously improved. The department of ophthalmology is a part of Sahlgrenska Hospital that heavily uses OCT for helping people with the treatment of eye diseases. They are currently facing a problem where the time to go from an OCT scan to treatment is being increased due to having an overload of patient visits every day. Since it requires a trained expert to analyze each OCT scan, the increase of patients is too overwhelming for the few experts that the department has. It is believed that the next phase of this medical field will be through the adoption of machine learning technology. This thesis has been issued by Sahlgrenska University Hospital (SUH), and they want to address the problem that ophthalmology has by introducing the use of machine learning into their workflow. This thesis aims to determine the best suited CNN through training and testing of pre-trained models and to build a tool that a model can be integrated into for use in ophthalmology. Transfer learning was used to compare three different types of pre-trained models offered by Keras, namely VGG16, InceptionResNet50V2 and ResNet50V2. They were all trained on an open dataset containing 84495 OCT images categorized into four different classes. These include the three diseases Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), drusen and normal eyes. To further improve the accuracy of the models, oversampling, undersampling, and data augmentation were applied to the training set and then tested in different variations. A web application was built using Tensorflow.js and Node.js that the best-performed model later was integrated into. The VGG16 model performed the best with only oversampling applied out of the three. It yielded an average of 95% precision, 95% recall and got a 95% F1-score. The second was the Inception model with only oversampling applied that got an average of 93% precision, 93% recall and a 93% F1-score. Last came the ResNet model with an average of 93% precision, 92% recall and a 92% F1-score. The results suggest that oversampling is the overall best technique for this given dataset. The chosen data augmentation techniques only lead to models performing marginally worse in all cases. It also suggests that pre-trained models with more parameters, such as the VGG16 model, have more feature mappings and, therefore, achieve higher accuracy. On this basis, parameters and better mappings of features should be taken into account when using pre-trained models.

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