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A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among all the medica

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial-volume-based over probabi ...

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a ...

In vivo fetal magnetic resonance imaging provides a unique approach for the study of early human brain development [1]. In utero cerebral morphometry could potentially be used as a marker of the cerebral maturation and help to distinguish between normal and abnormal development in ambiguous situations. However, this quantitative approach is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution provided by very fast MRI sequences and the partial volume

Introduction. Development of the fetal brain surface with concomitant gyrification is one of the major maturational processes of the human brain. First delineated by postmortem studies or by ultrasound, MRI has recently become a powerful tool for studying in vivo the structural correlates of brain maturation. However, the quantitative measurement of fetal brain development is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution, the partial volume ef

Motivation. The study of human brain development in its early stage is today possible thanks to in vivo fetal magnetic resonance imaging (MRI) techniques. A quantitative analysis of fetal cortical surface represents a new approach which can be used as a marker of the cerebral maturation (as gyration) and also for studying central nervous system pathologies [1]. However, this quantitative approach is a major challenge for several reasons. First, movement of the fetus inside the amniotic cavity requires very

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to comb ...

Objective Cortical lesions are common in multiple sclerosis (MS), but their visualization is challenging on conventional magnetic resonance imaging. The uniform image derived from magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE(uni)) de ...

The relation of white matter hyperintense lesions to episodic memory impairment in patients with Parkinson's disease (PD) is still controversial. We aimed at evaluating the relation between white matter hyperintense lesions and episodic memory decline in p ...

Abstract Multiple sclerosis diagnostic criteria lack optimal specificity, leading to potential misdiagnosis. Advanced magnetic resonance imaging (MRI) biomarkers like the central vein sign, cortical lesions and paramagnetic rim lesions are highly specific to multiple sclerosis and could potentially improve diagnostic accuracy. In this study, we applied machine learning techniques to a retrospective, multicentric dataset of 322 multiple sclerosis/multiple sclerosis-mimic (204/118) and 84 prodromal multiple s

In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models’ decision mechanisms in the case of classification and even for segmentation. However, XAI methods for semantic segmentation and in particular for single specific instances (e.g. one given lesion among others of the same class in medical imaging) have yet to be developed to understand what drove the detection and contouring of the latter, which is crucial for all multi-lesional diseases. We propos

In this paper we present a new method to track bone movements in stereoscopic X-ray image series of the knee joint. The method is based on two different X-ray image sets: a rotational series of acquisitions of the still subject knee that will allow the tom ...

This site provides two software tools related to "RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis" by Barquero et al. NeuroImage: Clinical (2020). People using in part or f ...

Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inﬂammatory activity and lesion load. In quantitative analysis of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability. Our aim

Objective To assess whether chronic white matter inflammation in patients with multiple sclerosis (MS) as detected in vivo by paramagnetic rim MRI lesions (PRLs) is associated with higher serum neurofilament light chain (sNfL) levels, a marker of neuroaxon ...

Introduction: During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given patholog ...

We propose a deep study on tissue modelization and classification techniques on T1-weighted MR images. Six approaches have been taken into account to perform this validation study. We consider first the Finite Gaussian Mixture model (A-FGMM) and a Bayes classification. Second method is the same as A-FGMM but introducing a Hidden Markov Random Field (HMRF) model to encode spatial information and classification is then performed by Maximum a Posteriori (MAP). Third, we study a method that models mixture tissu

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based ...

Magnetic resonance imaging (MRI) is a powerful modality for investigating abnormal developmental patterns in utero. However, since it is not the first-line diagnostic tool in this sensitive population, data remain scarce and heterogeneous across scanners a ...

Development of technologies in the field of neuroimaging brought an increase interest in the quantification of cortical folding (gyrification), notably for neurogenetic and psychiatric disorders that may result from an early abnormal cortical development. For example, gyrification has been reported altered in schizophrenia (Kulynych, 1997; Harris, 2004), autism (Hardan, 2004), and Williams syndrome (Schmitt, 2002). Algorithms traditionally used to quantify gyrification measure the cortical perimeter on two-

Meritxell Bach Cuadra

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Atlas-based segmentation and classification of magnetic resonance brain images

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

Quantitative morphometry analysis of the fetal brain using clinical MR imaging

A post-processing pipeline to reconstruct the developing fetal brain using low-resolution MRI

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Model-informed machine learning for multi-component T2 relaxometry

Fluid and White Matter Suppression New Sensitive 3 T Magnetic Resonance Imaging Contrasts for Cortical Lesion Detection in Multiple Sclerosis

Episodic memory decline in Parkinson' s disease: relation with white matter hyperintense lesions and influence of quantification method

Machine learning-based combination of the central vein sign, cortical lesions and paramagnetic rim lesions: a web-based tool for the diagnosis of multiple sclerosis

Instance-level quantitative saliency in multiple sclerosis lesion segmentation

Bi-planar 2D-to-3D Registration in Fourier Domain for Stereoscopic X-Ray Motion Tracking

Streamline RimNet: Tools for Automatic Classification of Paramagnetic Rim Lesions in MRI of Multiple Sclerosis

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Chronic White Matter Inflammation and Serum Neurofilament Levels in Multiple Sclerosis

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

Validation of Tissue Modelization and Classification Techniques in T1-weighted MR Brain Images

A Multimodal Evaluation Method for Medical Image Segmentation

A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain

A New Method For Measuring Cortical Folding In A 3D Space From MR Images