Matthieu Perrot

Matthieu Perrot
L'Oréal · Research and Development

PhD

About

34
Publications
69,242
Reads
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84,221
Citations
Additional affiliations
December 2016 - present
L''Oréal
Position
  • Head of Department
November 2014 - November 2016
Philips
Position
  • Researcher
October 2011 - March 2014

Publications

Publications (34)
Article
The classification of MRI images according to the anatomical field of view is a necessary task to solve when faced with the increasing quantity of medical images. In parallel, advances in deep learning makes it a suitable tool for computer vision problems. Using a common architecture (such as AlexNet) provides quite good results, but not sufficient...
Article
Full-text available
Segregating the human cortex into distinct areas based on structural connectivity criteria is of widespread interest in neuroscience. This paper presents a groupwise connectivity-based parcellation framework for the whole cortical surface using a new high quality diffusion dataset of 79 healthy subjects. Our approach performs gyrus by gyrus to parc...
Chapter
Full-text available
The complexity and the variability of the cortical folding pattern is overwhelming for human experts. Computational anatomy helps the field to harness the folding variability considered as a proxy for architectural variability. First, bottom-up processing pipelines convert the implicit encoding of the cortical folding pattern embedded in the geomet...
Article
Full-text available
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very limited relevance. A penalty like Tot...
Article
Developmental research, as well as paediatric clinical activity crucially depends on non-invasive and painless brain recording techniques, such as electroencephalography (EEG), and near infrared spectroscopy (NIRS). However, both of these techniques measure cortical activity from the scalp without precise knowledge of the recorded cerebral structur...
Article
Prenatal alcohol exposure is responsible for a broad range of brain structural malformations, which can be studied using magnetic resonance imaging (MRI). Advanced MRI methods have emerged to characterize brain abnormalities, but the teratogenic effects of alcohol on cortical morphology have received little attention to date. Twenty-four 9-year-old...
Conference Paper
Full-text available
Splitting the cortical surface into regions with homogeneous dMRI-based connectivity profiles is a promising but challenging topic. This paper extends the inter-subject connectivity-based cortex parcellation framework proposed by Roca [1]. In a first step, we implement the state-of-the-art algorithm with tuned parameters and, then propose a refined...
Article
In the context of inter subject brain surface matching, we present a parameterization of the cortical surface constrained by a model of cortical organization. The parameterization is defined via an harmonic mapping of each hemisphere surface to a rectangular planar domain that integrates a representation of the model. As opposed to previous landmar...
Conference Paper
In the context of the study of brain morphogenesis, we present here a framework for surface-based group analysis using local cortical features in the neonate brain. We propose to detect and match local maxima from curvature and depth profiles of sulcal fundi. Such entities could have a key role to understand the variability of the brain morphology....
Article
Sinistrals differ from dextrals in the size of certain cortical folds. For instance, handedness has an impact on central sulcus surface area: the sulcus is larger in the dominant left hemisphere of dextrals and vice versa for sinistrals. However, the impact of handedness on the shape of the central sulcus is largely unexplored. In this paper, we pr...
Article
Full-text available
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consiste...
Article
Full-text available
The alignment and normalization of individual brain structures is a prerequisite for group-level analyses of structural and functional neuroimaging data. The techniques currently available are either based on volume and/or surface attributes, with limited insight regarding the consistent alignment of anatomical landmarks across individuals. This ar...
Article
Apathy is a debilitating symptom in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), the pathophysiology of which remains poorly understood. The aim of this study was to evaluate the neuroanatomic correlates of apathy, using new MRI postprocessing methods based on the identification of cortical s...
Article
Brain mapping techniques pair similar anatomical information across individuals. In this context, spatial normalization is mainly used to reduce inter-subject differences to improve comparisons. These techniques may benefit from anatomically identified landmarks useful to drive the registration. Automatic labeling, classification or segmentation te...
Article
La mise en évidence de biomarqueurs spéciques de pathologies cérébrales à l'échelle d'une population reste extrêmement dicile compte tenu de la variabilité inter-individuelle de la topographie sulco-gyrale. Cette thèse propose de répondre à cette diculté par l'identication automatique de 125 structures sulcales et leur mise en correspondance au tra...
Article
Introduction La TEP offre des marqueurs particulièrement sensibles à l’évolution des patients du stade pré-démentiel de aMCI à une maladie d’Alzheimer probable. Néanmoins, l’IRM reste un outil moins invasif beaucoup plus accessible en clinique. L’objectif de cette étude était donc d’identifier les modifications morphologiques sulcales qui pourraien...
Conference Paper
Full-text available
Neuroimaging at the group level requires spatial normalization of individual structural data. We propose a geometric approach that consists in matching a series of cortical surfaces through diffeomorphic registration of their sulcal imprints. The resulting 3D transforms naturally extends to the entire MRI volumes. The Diffeomorphic Sulcal-based COr...
Conference Paper
Full-text available
Brain imaging provides a wealth of information that computers can explore at a massive scale. Categorizing the patterns of the human cortex has been a challenging issue for neuroscience. In this paper, we propose a data mining approach leading to the construction of the first computerized dictionary of cortical folding patterns, from a database of...
Conference Paper
Full-text available
A new approach for fMRI group data analysis is introduced to overcome the limitations of standard voxel-based testing methods, such as Statistical Parametric Mapping (SPM). Using a Bayesian model selection framework, the functional network associated with a certain cognitive task is selected according to the posterior probabilities of mean region a...
Conference Paper
In this paper, we study the recognition of about 60 sulcal structures over a new T1 MRI database of 62 subjects. It continues our previous work [7] and more specifically extends the localization model of sulci (SPAM). This model is sensitive to the chosen common space during the group study. Thus, we focus the current work on refining this space us...
Conference Paper
In this paper we propose an approach to identify sulci from sulcal pieces. Our method is founded on the sulci localization, feature-based shapes and their local organization. The position data enable the devising of an easy handled 3D probabilistic atlas using SPAM models. Shapes and local sulci scheme are recognized thanks to SVR models (a regress...
Conference Paper
Full-text available
Understanding brain structure and function entails the inclusion of anatomical and functional information in a common space, in order to study how these different informations relate to each other in a population of subjects. In this paper, we revisit the parcellation model and explicitly combine anatomical features, i.e. a segmentation of the cort...

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