Meritxell Bach Cuadra
École Polytechnique Fédérale de Lausanne
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Featured researches published by Meritxell Bach Cuadra.
IEEE Transactions on Medical Imaging | 2008
Marie Schaer; Meritxell Bach Cuadra; Lucas Tamarit; François Lazeyras; Stephane Eliez; Jean-Philippe Thiran
The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.
Computer Methods and Programs in Biomedicine | 2011
Mariano Cabezas; Arnau Oliver; Xavier Lladó; Jordi Freixenet; Meritxell Bach Cuadra
Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.
Schizophrenia Research | 2009
M. Schaer; Martin Debbané; Meritxell Bach Cuadra; Marie-Christine Ottet; Bronwyn Glaser; Jean-Philippe Thiran; Stephan Eliez
22q11.2 deletion syndrome (22q11DS) is associated with an increased susceptibility to develop schizophrenia. Despite a large body of literature documenting abnormal brain structure in 22q11DS, cerebral changes associated with brain maturation in 22q11DS remained largely unexplored. To map cortical maturation from childhood to adulthood in 22q11.2 deletion syndrome, we used cerebral MRI from 59 patients with 22q11DS, aged 6 to 40, and 80 typically developing controls; three year follow-up assessments were also available for 32 patients and 31 matched controls. Cross-sectional cortical thickness trajectories during childhood and adolescence were approximated in age bins. Repeated-measures were also conducted with the longitudinal data. Within the group of patients with 22q11DS, exploratory measures of cortical thickness differences related to COMT polymorphism, IQ, and schizophrenia were also conducted. We observed deviant trajectories of cortical thickness changes with age in patients with 22q11DS. In affected preadolescents, larger prefrontal thickness was observed compared to age-matched controls. Afterward, we observed greater cortical loss in 22q11DS with a convergence of cortical thickness values by the end of adolescence. No compelling evidence for an effect of COMT polymorphism on cortical maturation was observed. Within 22q11DS, significant differences in cortical thickness were related to cognitive level in children and adolescents, and to schizophrenia in adults. Deviant trajectories of cortical thickness from childhood to adulthood provide strong in vivo cues for a defect in the programmed synaptic elimination, which in turn may explain the susceptibility of patients with 22q11DS to develop psychosis.
Developmental Medicine & Child Neurology | 2009
Marie Schaer; Bronwyn Glaser; Meritxell Bach Cuadra; Martin Debbané; Jean-Philippe Thiran; Stephan Eliez
22q11.2 deletion syndrome (22q11.2DS) is a common genetic condition associated with cognitive and learning impairments. In this study, we applied a three‐dimensional method for quantifying gyrification at thousands of points over the cortical surface to imaging data from 44 children, adolescents, and young adults with 22q11.2DS (17 males, 27 females; mean age 17y 2mo [SD 9y 1mo], range 6–37y), and 53 healthy participants (21 males, 32 females; mean age 15y 4mo [SD 8y 6mo]; range 6–40y). Several clusters of reduced gyrification were observed, further substantiating the pattern of cerebral alterations presented by children with the syndrome. Comparisons within 22q11.2DS demonstrated an effect of congenital heart disease (CHD) on cortical gyrification, with reduced gyrification at the parieto‐temporo‐occipital junction in patients with CHD, as compared with patients without CHD. Reductions in gyrification can resemble mild polymicrogyria, suggesting early abnormal neuronal proliferation or migration and providing support for an effect of hemodynamic factors on brain development in 22q11.2DS. The results also shed light on the pathophysiology of acquired brain injury in other populations with CHD.
Journal of Neurodevelopmental Disorders | 2010
Marie Schaer; Bronwyn Glaser; Marie-Christine Ottet; Maude Schneider; Meritxell Bach Cuadra; Martin Debbané; Jean-Philippe Thiran; Stephan Eliez
Children with congenital heart disease (CHD) who survive surgery often present impaired neurodevelopment and qualitative brain anomalies. However, the impact of CHD on total or regional brain volumes only received little attention. We address this question in a sample of patients with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition frequently associated with CHD. Sixty-one children, adolescents, and young adults with confirmed 22q11.2 deletion were included, as well as 80 healthy participants matched for age and gender. Subsequent subdivision of the patients group according to CHD yielded a subgroup of 27 patients with normal cardiac status and a subgroup of 26 patients who underwent cardiac surgery during their first years of life (eight patients with unclear status were excluded). Regional cortical volumes were extracted using an automated method and the association between regional cortical volumes, and CHD was examined within a three-condition fixed factor. Robust protection against type I error used Bonferroni correction. Smaller total cerebral volumes were observed in patients with CHD compared to both patients without CHD and controls. The pattern of bilateral regional reductions associated with CHD encompassed the superior parietal region, the precuneus, the fusiform gyrus, and the anterior cingulate cortex. Within patients, a significant reduction in the left parahippocampal, the right middle temporal, and the left superior frontal gyri was associated with CHD. The present results of global and regional volumetric reductions suggest a role for disturbed hemodynamic in the pathophysiology of brain alterations in patients with neurodevelopmental disease and cardiac malformations.
Medical Image Analysis | 2011
Sai Subrahmanyam Gorthi; Valerie Duay; Xavier Bresson; Meritxell Bach Cuadra; F. Javier Sánchez Castro; Claudio Pollo; Abdelkarim Said Allal; Jean-Philippe Thiran
This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.
NeuroImage | 2015
Sébastien Tourbier; Xavier Bresson; Patric Hagmann; Jean-Philippe Thiran; Reto Meuli; Meritxell Bach Cuadra
Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.
Journal of Magnetic Resonance Imaging | 2016
Mário João Fartaria; Guillaume Bonnier; Alexis Roche; Tobias Kober; Reto Meuli; David Rotzinger; Richard S. J. Frackowiak; Myriam Schluep; Renaud Du Pasquier; Jean-Philippe Thiran; Gunnar Krueger; Meritxell Bach Cuadra; Cristina Granziera
To develop a method to automatically detect multiple sclerosis (MS) lesions, located both in white matter (WM) and in the cortex, in patients with low disability and early disease stage.
PLOS ONE | 2014
Anca Ciurte; Xavier Bresson; Olivier Cuisenaire; Nawal Houhou; Sergiu Nedevschi; Jean-Philippe Thiran; Meritxell Bach Cuadra
Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.
IEEE Signal Processing Letters | 2013
Sai Subrahmanyam Gorthi; Meritxell Bach Cuadra; Pierre-Alain Tercier; Abdelkarim Said Allal; Jean-Philippe Thiran
In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: “Global Weighted Shape-Based Averaging” (GWSBA) and “Local Weighted Shape-Based Averaging” (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.