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2. A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

Author

Virgilio Kmetzsch, Emmanuelle Becker, Dario Saracino, Vincent Anquetil, Daisy Rinaldi, Agnès Camuzat, Thomas Gareau, Isabelle Le Ber, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL), FRONTLAB: Fonctions et dysfonctions de systèmes frontaux [ICM Paris] (FRONTlab), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-14-CE15-0016,PREV-DEMALS,Prédire pour prévenir les démences frontotemporales (DFT) et la sclérose latérale amyotrophique (SLA)(2014), Kmetzsch, Virgilio, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Appel à projets générique - Prédire pour prévenir les démences frontotemporales (DFT) et la sclérose latérale amyotrophique (SLA) - - PREV-DEMALS2014 - ANR-14-CE15-0016 - Appel à projets générique - VALID, Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], and École pratique des hautes études (EPHE)

Subjects
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Multimodal, Deep learning, MicroRNA, Neuroimaging, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Variational autoencoder, Neurodegenerative disease, Transcriptomics, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Disease progression score
Abstract

International audience; Frontotemporal dementia (FTD) is a rare neurodegenerative disease, often of genetic origin, with no effective treatment. There is a substantial pathophysiological overlap with amyotrophic lateral sclerosis (ALS), mutations in the C9orf72 gene being their most common genetic cause. In these disorders, no single biomarker can accurately measure progression, thus it is crucial to combine complementary information from multiple modalities to evaluate new therapeutic interventions. In particular, neuroimaging and transcriptomic (microRNA) data have been shown to have value to track FTD and ALS progression. As these conditions are rare, large samples are not available, hence the need for methods to fuse multimodal data from small samples. In this paper, we propose a method for computing a disease progression score (DPS) from cross-sectional multimodal data, based on variational autoencoders (VAE). We show that unsupervised training leads to the estimation of meaningful latent spaces, where subjects with similar disease states are clustered together and from which a DPS may be inferred. Models were evaluated on 14 patients, 40 presymptomatic mutation carriers and 37 healthy controls from the PREV-DEMALS study. Since there is no ground truth for the DPS, we used the inferred scores to perform pairwise classification as a proxy metric. Presymptomatic subjects and patients were classified with an average area under the ROC curve of 0.83 and 0.94, respectively without and with feature selection. The proposed approach has the potential to leverage cross-sectional multimodal datasets with small sample sizes in order to objectively measure disease progression.

Published
2022
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3. Description of brain internal structures by means of spatial relations for MR image segmentation

Author

Olivier Colliot, Isabelle Bloch, Remi Dewynter, and Oscar Camara

Subjects
Structure (mathematical logic), Spatial relation, Relation (database), business.industry, Fuzzy set, Initialization, Scale-space segmentation, Computer vision, Segmentation, Image segmentation, Artificial intelligence, business, Mathematics
Abstract

This paper presents a method for segmenting internal brain structures in MR images. It introduces prior information in an original way through descriptions of the spatial arrangement of structures by means of spatial relations, which are represented in the fuzzy set framework. The method is hierarchical as the segmentation of a given structure is based on the previously segmented ones. The processing of each structure is decomposed into two stages: an initialization stage which makes extensive use of prior knowledge and a refinement stage using a 3D deformable model. The deformable model is guided by an external force representing the combination of a classical data term derived from an edge map and a force corresponding to a given spatial relation. We propose different ways to compute a force from a fuzzy set representing a relation or a combination of relations. Results obtained for the lateral ventricles, the third ventricle, the caudate nuclei and the thalami are promising. The proposed combination of spatial relations and deformable models has proved to be very useful to segment parts of the structures were no visible edges are present, improving the segmentation accuracy.

Published
2004
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