An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run

Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practice

Neuroimaging offers an unmatched description of the brain’s structure and physiology, but the information it provides is not easy to extract and interpret. A popular way to extract meaningful information from brain images is to use computational methods based on machine learning and deep learning to predict the current or future diagnosis of a patient. A large number of these approaches have been dedicated to the computer-aided diagnosis of dementia, and more specifically of Alzheimer's disease. However, only a few are translated to the clinic. This can be explained by different factors such as the lack of rigorous validation of these approaches leading to over-optimistic performance and their lack of reproducibility, but also the limited interpretability of these methods and their limited generalisability when moving from highly controlled research data to routine clinical data. This manuscript describes how we tried to address these limitations.We have proposed reproducible frameworks for the evaluation of Alzheimer's disease classification methods and developed two open-source software platforms for clinical neuroimaging studies (Clinica) and neuroimaging processing with deep learning (ClinicaDL). We have implemented and assessed the robustness of a visualisation method aiming to interpret convolutional neural networks and used it to study the stability of the network training. We concluded that, currently, combining a convolutional neural networks classifier with an interpretability method may not constitute a robust tool for individual computer-aided diagnosis. As an alternative, we have proposed an approach that detects anomalies in the brain by generating what would be the healthy version of a patient's image and comparing this healthy version with the real image. Finally, we have studied the performance of machine and deep learning algorithms for the computer-aided diagnosis of dementia from images acquired in clinical routine.La neuro-imagerie offre une description inégalée de la structure et de la physiologie du cerveau, mais les informations qu'elle fournit ne sont pas faciles à extraire et à interpréter. Une façon populaire d'extraire des informations pertinentes d'images cérébrales consiste à utiliser des méthodes basées sur l'apprentissage statistique et l'apprentissage profond pour prédire le diagnostic actuel ou futur d'un patient. Un grand nombre de ces approches ont été dédiées au diagnostic assisté par ordinateur de la démence, et plus spécifiquement de la maladie d'Alzheimer. Cependant, seules quelques-unes sont transposées en clinique. Cela peut s'expliquer par différents facteurs tels que l'absence de validation rigoureuse de ces approches conduisant à des performances trop optimistes et à leur manque de reproductibilité, mais aussi l'interprétabilité limitée de ces méthodes et leur généralisation limitée lors du passage de données de recherche hautement contrôlées à des données cliniques de routine. Ce manuscrit décrit comment nous avons tenté de remédier à ces limites.Nous avons proposé des cadres reproductibles pour l'évaluation des méthodes de classification de la maladie d'Alzheimer et développé deux plateformes logicielles open-source pour les études de neuroimagerie clinique (Clinica) et le traitement de la neuroimagerie par apprentissage profond (ClinicaDL). Nous avons implémenté et évalué la robustesse d'une méthode de visualisation visant à interpréter les réseaux neuronaux convolutifs et l'avons utilisée pour étudier la stabilité de l'entraînement du réseau. Nous avons conclu qu'actuellement, la combinaison de réseaux neuronaux convolutifs avec une méthode d'interprétabilité peut ne pas constituer un outil robuste pour le diagnostic individuel assisté par ordinateur. De façon alternative, nous avons proposé une approche qui détecte les anomalies dans le cerveau en générant ce qui serait la version saine de l'image d'un patient et en comparant cette version saine avec l'image réelle. Enfin, nous avons étudié les performances des algorithmes d'apprentissage statistique et profond pour le diagnostic assisté par ordinateur de la démence à partir d'images acquises en routine clinique

An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

International audienceWe present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run