Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.

La maladie d’Alzheimer est la première cause de démence chez les personnes âgées. Cette maladie est caractérisée par un déclin irréversible des fonctions cognitives. Les patients atteints par la maladie d’Alzheimer ont de sévères pertes de mémoire et ont de grandes difficultés à apprendre de nouvelles informations ce qui pose de gros problèmes dans leur vie quotidienne. À ce jour, cette maladie est diagnostiquée après que d’importantes altérations des structures du cerveaux apparaissent. De plus, aucune thérapie existe permettant de faire reculer ou de stopper la maladie. Le développement de nouvelles méthodes permettant la détection précoce de cette maladie est ainsi nécessaire. En effet, une détection précoce permettrait une meilleure prise en charge des patients atteints de cette maladie ainsi qu’une accélération de la recherche thérapeutique. Nos travaux de recherche portent sur l’utilisation de l’imagerie médicale, avec notamment l’imagerie par résonance magnétique (IRM) qui a démontrée ces dernières années son potentiel pour améliorer la détection et la prédiction de la maladie d’Alzheimer. Afin d’exploiter pleinement ce type d’imagerie, de nombreuses méthodes ont été proposées récemment. Au cours de nos recherches, nous nous sommes intéressés à un type de méthode en particulier qui est basé sur la correspondance de patchs dans de grandes bibliothèques d’images. Nous avons étudié ces méthodes à diverses échelles anatomiques c’est à dire, cerveaux entier, hippocampe, sous-champs de l’hippocampe) avec diverses modalités d’IRM (par exemple, IRM anatomique et imagerie de diffusion). Nous avons amélioré les performances de détection dans les stades les plus précoces avec l’imagerie par diffusion. Nous avons aussi proposé un nouveau schéma de fusion pour combiner IRM anatomique et imagerie de diffusion. De plus, nous avons montré que la correspondance de patchs était améliorée par l’utilisation de filtres dérivatifs. Enfin, nous avons proposé une méthode par graphe permettant de combiner les informations de similarité inter-sujet avec les informations apportées par la variabilité intra-sujet. Les résultats des expériences menées dans cette thèse ont montrées une amélioration des performances de diagnostique et de prognostique de la maladie d’Alzheimer comparé aux méthodes de l’état de l’art. / Alzheimer’s disease (AD) is the most common dementia leading to a neurodegenerative process and causing mental dysfunctions. According to the world health organization, the number of patients having AD will double in 20 years. Neuroimaging studies performed on AD patients revealed that structural brain alterations are advanced when the diagnosis is established. Indeed, the clinical symptoms of AD are preceded by brain changes. This stresses the need to develop new biomarkers to detect the first stages of the disease. The development of such biomarkers can make easier the design of clinical trials and therefore accelerate the development of new therapies. Over the past decades, the improvement of magnetic resonance imaging (MRI) has led to the development of new imaging biomarkers. Such biomarkers demonstrated their relevance for computer-aided diagnosis but have shown limited performances for AD prognosis. Recently, advanced biomarkers were proposed toimprove computer-aided prognosis. Among them, patch-based grading methods demonstrated competitive results to detect subtle modifications at the earliest stages of AD. Such methods have shown their ability to predict AD several years before the conversion to dementia. For these reasons, we have had a particular interest in patch-based grading methods. First, we studied patch-based grading methods for different anatomical scales (i.e., whole brain, hippocampus, and hippocampal subfields). We adapted patch-based grading method to different MRI modalities (i.e., anatomical MRI and diffusion-weighted MRI) and developed an adaptive fusion scheme. Then, we showed that patch comparisons are improved with the use of multi-directional derivative features. Finally, we proposed a new method based on a graph modeling that enables to combine information from inter-subjects’ similarities and intra-subjects’ variability. The conducted experiments demonstrate that our proposed method enable an improvement of AD detection and prediction.

Para avaliar as características clínicas e evolutivas em pacientes com formas cardíacas iniciais assintomáticas da doença de Chagas, realizou-se estudo prospectivo em 108 pacientes com idade entre 18 e 50 anos, atendidos entre abril e novembro de 2002 no ambulatório de doença de Chagas da Universidade Estadual de Londrina. Os pacientes foram submetidos a 1)avaliação clínica, 2)eletrocardiograma (ECG), 3)radiografia de tórax e cálculo do índice cardio-torácico (ICT), 4)eletrocardiografia dinâmica de 24 horas, 5)ecocardiografia bidimensional com Doppler tecidual e 6)dosagem plasmática do peptídeo natriurético tipo B (BNP). Os pacientes foram divididos em 3 grupos: 50 no GI - ECG e ICT normais, 31 no GIIA - ECG com alterações características de doença de Chagas e 25 no GIIB - ECG com alterações não características de doença de Chagas / To evaluate clinical and evolutive features in patients with initial asymptomatic cardiac Chagas' disease, a prospective study was carried out with 108 patients, age 18 and 50, at the Londrina State University Chagas' disease outpatient clinic, from April to November 2002. Patients were submitted to: 1) clinical evaluation, 2) electrocardiography (EKG), 3) chest radiography and cardiothoracic index (CTI), 4)24-hour dynamic electrocardiography, 5) bi-dimensional echocardiography with tissued Doppler imaging and 6) type-B natriuretic peptide (BNP) plasmatic dosage. Patients were divided into 3 groups: GI - normal EKG and CTI (50 patients), GIIA - EKG with typical Chagas' disease alterations (31 patients) and GIIB - EKG with alterations not characteristic of Chagas´ disease (25 patients)

Para avaliar as características clínicas e evolutivas em pacientes com formas cardíacas iniciais assintomáticas da doença de Chagas, realizou-se estudo prospectivo em 108 pacientes com idade entre 18 e 50 anos, atendidos entre abril e novembro de 2002 no ambulatório de doença de Chagas da Universidade Estadual de Londrina. Os pacientes foram submetidos a 1)avaliação clínica, 2)eletrocardiograma (ECG), 3)radiografia de tórax e cálculo do índice cardio-torácico (ICT), 4)eletrocardiografia dinâmica de 24 horas, 5)ecocardiografia bidimensional com Doppler tecidual e 6)dosagem plasmática do peptídeo natriurético tipo B (BNP). Os pacientes foram divididos em 3 grupos: 50 no GI - ECG e ICT normais, 31 no GIIA - ECG com alterações características de doença de Chagas e 25 no GIIB - ECG com alterações não características de doença de Chagas / To evaluate clinical and evolutive features in patients with initial asymptomatic cardiac Chagas' disease, a prospective study was carried out with 108 patients, age 18 and 50, at the Londrina State University Chagas' disease outpatient clinic, from April to November 2002. Patients were submitted to: 1) clinical evaluation, 2) electrocardiography (EKG), 3) chest radiography and cardiothoracic index (CTI), 4)24-hour dynamic electrocardiography, 5) bi-dimensional echocardiography with tissued Doppler imaging and 6) type-B natriuretic peptide (BNP) plasmatic dosage. Patients were divided into 3 groups: GI - normal EKG and CTI (50 patients), GIIA - EKG with typical Chagas' disease alterations (31 patients) and GIIB - EKG with alterations not characteristic of Chagas´ disease (25 patients)