32 research outputs found
Unsupervised deep learning of human brain diffusion magnetic resonance imaging tractography data
L'imagerie par résonance magnétique de diffusion est une technique non invasive permettant de connaître la microstructure organisationnelle des tissus biologiques. Les méthodes computationnelles qui exploitent la préférence orientationnelle de la diffusion dans des structures restreintes pour révéler les voies axonales de la matière blanche du cerveau sont appelées tractographie. Ces dernières années, diverses méthodes de tractographie ont été utilisées avec succès pour découvrir l'architecture de la matière blanche du cerveau. Pourtant, ces techniques de reconstruction souffrent d'un certain nombre de défauts dérivés d'ambiguïtés fondamentales liées à l'information orientationnelle. Cela a des conséquences dramatiques, puisque les cartes de connectivité de la matière blanche basées sur la tractographie sont dominées par des faux positifs. Ainsi, la grande proportion de voies invalides récupérées demeure un des principaux défis à résoudre par la tractographie pour obtenir une description anatomique fiable de la matière blanche. Des approches méthodologiques innovantes sont nécessaires pour aider à résoudre ces questions.
Les progrès récents en termes de puissance de calcul et de disponibilité des données ont rendu possible l'application réussie des approches modernes d'apprentissage automatique à une variété de problèmes, y compris les tâches de vision par ordinateur et d'analyse d'images. Ces méthodes modélisent et trouvent les motifs sous-jacents dans les données, et permettent de faire des prédictions sur de nouvelles données. De même, elles peuvent permettre d'obtenir des représentations compactes des caractéristiques intrinsèques des données d'intérêt. Les approches modernes basées sur les données, regroupées sous la famille des méthodes d'apprentissage profond, sont adoptées pour résoudre des tâches d'analyse de données d'imagerie médicale, y compris la tractographie. Dans ce contexte, les méthodes deviennent moins dépendantes des contraintes imposées par les approches classiques utilisées en tractographie. Par conséquent, les méthodes inspirées de l'apprentissage profond conviennent au changement de paradigme requis, et peuvent ouvrir de nouvelles possibilités de modélisation, en améliorant ainsi l'état de l'art en tractographie.
Dans cette thèse, un nouveau paradigme basé sur les techniques d'apprentissage de représentation est proposé pour générer et analyser des données de tractographie. En exploitant les architectures d'autoencodeurs, ce travail tente d'explorer leur capacité à trouver un code optimal pour représenter les caractéristiques des fibres de la matière blanche. Les contributions proposées exploitent ces représentations pour une variété de tâches liées à la tractographie, y compris (i) le filtrage et (ii) le regroupement efficace sur les résultats générés par d'autres méthodes, ainsi que (iii) la reconstruction proprement dite des fibres de la matière blanche en utilisant une méthode générative. Ainsi, les méthodes issues de cette thèse ont été nommées (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), et (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectivement. Les performances des méthodes proposées sont évaluées par rapport aux méthodes de l'état de l'art sur des données de diffusion synthétiques et des données de cerveaux humains chez l'adulte sain in vivo. Les résultats montrent que (i) la méthode de filtrage proposée offre une sensibilité et spécificité supérieures par rapport à d'autres méthodes de l'état de l'art; (ii) le regroupement des tractes dans des faisceaux est fait de manière consistante; et (iii) l'approche générative échantillonnant des tractes comble mieux l'espace de la matière blanche dans des régions difficiles à reconstruire. Enfin, cette thèse révèle les possibilités des autoencodeurs pour l'analyse des données des fibres de la matière blanche, et ouvre la voie à fournir des données de tractographie plus fiables.Abstract : Diffusion magnetic resonance imaging is a non-invasive technique providing insights into the organizational microstructure of biological tissues. The computational methods that exploit the orientational preference of the diffusion in restricted structures to reveal the brain's white matter axonal pathways are called tractography. In recent years, a variety of tractography methods have been successfully used to uncover the brain's white matter architecture. Yet, these reconstruction techniques suffer from a number of shortcomings derived from fundamental ambiguities inherent to the orientation information. This has dramatic consequences, since current tractography-based white matter connectivity maps are dominated by false positive connections. Thus, the large proportion of invalid pathways recovered remains one of the main challenges to be solved by tractography to obtain a reliable anatomical description of the white matter. Methodological innovative approaches are required to help solving these questions. Recent advances in computational power and data availability have made it possible to successfully apply modern machine learning approaches to a variety of problems, including computer vision and image analysis tasks. These methods model and learn the underlying patterns in the data, and allow making accurate predictions on new data. Similarly, they may enable to obtain compact representations of the intrinsic features of the data of interest. Modern data-driven approaches, grouped under the family of deep learning methods, are being adopted to solve medical imaging data analysis tasks, including tractography. In this context, the proposed methods are less dependent on the constraints imposed by current tractography approaches. Hence, deep learning-inspired methods are suit for the required paradigm shift, may open new modeling possibilities, and thus improve the state of the art in tractography. In this thesis, a new paradigm based on representation learning techniques is proposed to generate and to analyze tractography data. By harnessing autoencoder architectures, this work explores their ability to find an optimal code to represent the features of the white matter fiber pathways. The contributions exploit such representations for a variety of tractography-related tasks, including efficient (i) filtering and (ii) clustering on results generated by other methods, and (iii) the white matter pathway reconstruction itself using a generative method. The methods issued from this thesis have been named (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), and (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectively. The proposed methods' performance is assessed against current state-of-the-art methods on synthetic data and healthy adult human brain in vivo data. Results show that the (i) introduced filtering method has superior sensitivity and specificity over other state-of-the-art methods; (ii) the clustering method groups streamlines into anatomically coherent bundles with a high degree of consistency; and (iii) the generative streamline sampling technique successfully improves the white matter coverage in hard-to-track bundles. In summary, this thesis unlocks the potential of deep autoencoder-based models for white matter data analysis, and paves the way towards delivering more reliable tractography data
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
Diffusion MRI tractography parcellation classifies streamlines into
anatomical fiber tracts to enable quantification and visualization for clinical
and scientific applications. Current tractography parcellation methods rely
heavily on registration, but registration inaccuracies can affect parcellation
and the computational cost of registration is high for large-scale datasets.
Recently, deep-learning-based methods have been proposed for tractography
parcellation using various types of representations for streamlines. However,
these methods only focus on the information from a single streamline, ignoring
geometric relationships between the streamlines in the brain. We propose
TractCloud, a registration-free framework that performs whole-brain
tractography parcellation directly in individual subject space. We propose a
novel, learnable, local-global streamline representation that leverages
information from neighboring and whole-brain streamlines to describe the local
anatomy and global pose of the brain. We train our framework on a large-scale
labeled tractography dataset, which we augment by applying synthetic transforms
including rotation, scaling, and translations. We test our framework on five
independently acquired datasets across populations and health conditions.
TractCloud significantly outperforms several state-of-the-art methods on all
testing datasets. TractCloud achieves efficient and consistent whole-brain
white matter parcellation across the lifespan (from neonates to elderly
subjects, including brain tumor patients) without the need for registration.
The robustness and high inference speed of TractCloud make it suitable for
large-scale tractography data analysis. Our project page is available at
https://tractcloud.github.io/.Comment: MICCAI 202
Adaptive microstructure-informed tractography for accurate brain connectivity analyses
Human brain has been subject of deep interest for centuries, given it's central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orientation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acquisition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro-structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure
The challenge of mapping the human connectome based on diffusion tractography
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations
Anisotropy Across Fields and Scales
This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018
Anisotropy Across Fields and Scales
This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018
Tractographie adaptative basée sur la microstructure pour des analyses précises de la connectivité cérébrale
Le cerveau est un sujet de recherche depuis plusieurs décennies, puisque son rôle
est central dans la compréhension du genre humain. Le cerveau est composé de
neurones, où leurs dendrites et synapses se retrouvent dans la matière grise alors que
les axones en constituent la matière blanche. L’information traitée dans les différentes
régions de la matière grise est ensuite transmise par l’intermédiaire des axones afin
d’accomplir différentes fonctions cognitives.
La matière blanche forme une structure d’interconnections complexe encore dif-
ficile à comprendre et à étudier. La relation entre l’architecture et la fonction du
cerveau a été étudiée chez les humains ainsi que pour d’autres espèces, croyant que
l’architecture des axones déterminait la dynamique du réseau fonctionnel.
Dans ce même objectif, l’Imagerie par résonance (IRM) est un outil formidable
qui nous permet de visualiser les tissus cérébraux de façon non-invasive. Plus partic-
ulièrement, l’IRM de diffusion permet d’estimer et de séparer la diffusion libre de celle
restreinte par la structure des tissus. Cette mesure de restriction peut être utilisée
afin d’inférer l’orientation locale des faisceaux de matière blanche. L’algorithme de
tractographie exploite cette carte d’orientation pour reconstruire plusieurs connexions
de la matière blanche (nommées “streamlines”).
Cette modélisation de la matière blanche permet d’estimer la connectivité cérébrale
dite structurelle entre les différentes régions du cerveau. Ces résultats peuvent être
employés directement pour la planification chirurgicale ou indirectement pour l’analyse
ou une Ă©valuation clinique.
Malgré plusieurs de ses limitations, telles que sa variabilité et son imprécision, la
tractographie reste l’unique moyen d’étudier l’architecture de la matière blanche ainsi
que la connectivité cérébrale de façon non invasive.
L’objectif de ce projet de doctorat est de répondre spécifiquement à ces limitations
et d’améliorer la précision anatomique des estimations de connectivité structurelle.
Dans ce but, nous avons développé un algorithme d’optimisation globale qui exploite
les informations de micro et macrostructure, en introduisant une procédure itéra-
tive qui utilise les propriétés sous-jacentes des tissus pour piloter la reconstruction
en utilisant une approche semi-globale. Ensuite, nous avons étudié la possibilité
d’adapter dynamiquement la position d’un ensemble de lignes de courant candidates
tout en intégrant le préalable anatomique de la douceur des trajectoires et en adap-
tant la configuration en fonction des données observées. Enfin, nous avons introduit
le concept de bundle-o-graphy en mettant en œuvre une méthode pour modéliser des
groupes de lignes de courant basées sur le concept que les axones sont organisés en
fascicules, en adaptant leur forme et leur Ă©tendue en fonction de la microstructure
sous-jacente.Abstract : Human brain has been subject of deep interest for centuries, given it’s central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orien- tation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acqui- sition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro- structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure.Sommario : Il cervello umano è oggetto di profondo interesse da secoli, dato il suo ruolo centrale
nel controllare e dirigere le azioni e le funzioni del corpo in risposta a stimoli
esterno. Il tessuto neurale è costituito principalmente da neuroni che, insieme ai dendriti
e alle sinapsi nervose, costituiscono la materia grigia (GM), la quale riveste un
ruolo centrale nelle funzioni cognitive. Le informazioni processate nella GM viaggiano
da una regione all’altra del cervello lungo estensioni delle cellule nervose, chiamate
assoni. Tutti insieme costituiscono la materia bianca (WM) la cui organizzazione
strutturale rimane tuttora sconosciuta. Il legame tra struttura e funzione del cervello
sono stati studiati a fondo su esseri umani e animali partendo dal presupposto che
l’architettura anatomica determini la dinamica della rete funzionale. In risposta a
ciò, sono emerse diverse tecniche di imaging, tra cui la risonanza magnetica pesata
per diffusione (DW-MRI) ha suscitato enormi speranze e aspettative. Questa tecnica
misura la diffusione sia libera che ristretta, ovvero il grado di libertĂ di movimento
delle molecole d’acqua, consentendo di mappare l’architettura delle fibre neuronali
in vivo e in maniera non invasiva. Basata su dati DW-MRI, la trattografia è in
grado di sfruttare le informazioni sull’orientamento locale delle fibre per ricostruirne
i percorsi a livello globale. Questo, a sua volta, consente di estrarre la connettivitĂ
strutturale della WM, utilizzata in diverse applicazioni cliniche come per diagnosi,
dissezioni virtuali e pianificazione chirurgica. Tuttavia, nonostante questa capacitĂ
unica e promettente, l’acquisizione dei dati soffre ancora di limitazioni tecniche
e recenti studi hanno messo in evidenza la scarsa accuratezza anatomica delle ricostruzioni
ottenute con questa tecnica, mettendone in dubbio l’efficacia per lo studio
della connettività cerebrale. Il focus di questo progetto di dottorato è quello di affrontare in modo specifico
queste limitazioni e di migliorare l’accuratezza anatomica delle stime di connettivitĂ
strutturale. A tal fine, abbiamo sviluppato un algoritmo di ottimizzazione globale che
sfrutta le informazioni sia micro che macrostrutturali, introducendo una procedura
iterativa che utilizza le proprietĂ del tessuto neuronale per guidare la ricostruzione utilizzando
un approccio semi-globale. Successivamente, abbiamo studiato la possibilitĂ
di adattare dinamicamente la posizione di un insieme di streamline candidate incorporando
il prior anatomico per cui devono seguire traiettorie regolari e adattando
la configurazione in base ai dati osservati. Infine, abbiamo introdotto il concetto
di bundle-o-graphy implementando un metodo per modellare gruppi di streamline
basato sul concetto che gli assoni sono organizzati in fasci, adattando la loro forma
ed estensione in base alla microstruttura sottostante