Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis
Mostra/ Apri
Creato da
Marzullo, Aldo
Leone, Nicola
Calimeri, Francesco
Terracina, Giorgio
Metadata
Mostra tutti i dati dell'itemDescrizione
Formato
/
Università della Calabria
Dipartimento di Matematica e Informatica
(DeMaCS)
Dottorato di Ricerca in Matematica e
Informatica - XXXII Ciclo; Multiple sclerosis (MS) is a chronic disease of the central nervous system, leading cause of
nontraumatic disability in young adults. MS is characterized by inflammation, demyelination and
neurodegenrative pathological processes which cause a wide range of symptoms, including cognitive
deficits and irreversible disability. Concerning the diagnosis of the disease, the introduction of
Magnetic Resonance Imaging (MRI) has constituted an important revolution in the last 30 years.
Furthermore, advanced MRI techniques, such as brain volumetry, magnetization transfer imaging
(MTI) and diffusion-tensor imaging (DTI) are nowadays the main tools for detecting alterations
outside visible brain lesions and contributed to our understanding of the pathological mechanisms
occurring in normal appearing white matter. In particular, new approaches based on the representation
of MR images of the brain as graph have been used to study and quantify damages in the
brain white matter network, achieving promising results.
In the last decade, novel deep learning based approaches have been used for studying social
networks, and recently opened new perspectives in neuroscience for the study of functional and
structural brain connectivity. Due to their effectiveness in analyzing large amount of data, detecting
latent patterns and establishing functional relationships between input and output, these
artificial intelligence techniques have gained particular attention in the scientific community and
is nowadays widely applied in many context, including computer vision, speech recognition, medical
diagnosis, among others.
In this work, deep learning methods were developed to support biomedical image analysis, in
particular for the classification and the characterization of MS patients based on structural connectivity
information. Graph theory, indeed, constitutes a sensitive tool to analyze the brain networks
and can be combined with novel deep learning techniques to detect latent structural properties
useful to investigate the progression of the disease.
In the first part of this manuscript, an overview of the state of the art will be given. We will
focus our analysis on studies showing the interest of DTI for WM characterization in MS. An
overview of the main deep learning techniques will be also provided, along with examples of
application in the biomedical domain.
In a second part, two deep learning approaches will be proposed, for the generation of new,
unseen, MRI slices of the human brain and for the automatic detection of the optic disc in retinal
fundus images.
In the third part, graph-based deep learning techniques will be applied to the study of brain
structural connectivity of MS patients. Graph Neural Network methods to classify MS patients
in their respective clinical profiles were proposed with particular attention to the model interpretation,
the identification of potentially relevant brain substructures, and to the investigation of the
importance of local graph-derived metrics for the classification task. Semisupervised and unsupervised
approaches were also investigated with the aim of reducing the human intervention in the
pipeline.; Université Claude Bernard Lyon 1
École Doctorale ED205 - Interdisciplinaire
Sciences Santé
Spécialité de doctorat :
Recherche clinique, innovation
technologique, santé publiqueRelazione
INF/01;