Search Results for author: Meritxell Bach Cuadra

Found 25 papers, 6 papers with code

Direct segmentation of brain white matter tracts in diffusion MRI

no code implementations5 Jul 2023 Hamza Kebiri, Ali Gholipour, Meritxell Bach Cuadra, Davood Karimi

The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.

Brain Segmentation

Robust thalamic nuclei segmentation from T1-weighted MRI

1 code implementation14 Apr 2023 Julie P. Vidal, Lola Danet, Patrice Péran, Jérémie Pariente, Meritxell Bach Cuadra, Natalie M. Zahr, Emmanuel J. Barbeau, Manojkumar Saranathan

HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for T1w images (T1w-THOMAS).

FetMRQC: Automated Quality Control for fetal brain MRI

no code implementations12 Apr 2023 Thomas Sanchez, Oscar Esteban, Yvan Gomez, Elisenda Eixarch, Meritxell Bach Cuadra

Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies.

Image Quality Assessment

Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation

1 code implementation10 Feb 2023 Vatsal Raina, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Henning Muller, Meritxell Bach Cuadra, Mark Gales

This work describes a detailed analysis of the recently proposed normalised Dice Similarity Coefficient (nDSC) for binary segmentation tasks as an adaptation of DSC which scales the precision at a fixed recall rate to tackle this bias.

Lesion Segmentation

Self-Supervised Isotropic Superresolution Fetal Brain MRI

no code implementations11 Nov 2022 Kay Lächler, Hélène Lajous, Michael Unser, Meritxell Bach Cuadra, Pol del Aguila Pla

In this paper, we sidestep this difficulty by providing a proof of concept of a self-supervised single-volume superresolution framework for T2-weighted FBMRI (SAIR).

Anatomy Image Reconstruction

Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI

no code implementations17 Sep 2022 Athena Taymourtash, Hamza Kebiri, Ernst Schwartz, Karl-Heinz Nenning, Sebastien Tourbier, Gregor Kasprian, Daniela Prayer, Meritxell Bach Cuadra, Georg Langs

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero.

Slice estimation in diffusion MRI of neonatal and fetal brains in image and spherical harmonics domains using autoencoders

no code implementations29 Aug 2022 Hamza Kebiri, Gabriel Girard, Yasser Aleman-Gomez, Thomas Yu, Andras Jakab, Erick Jorge Canales-Rodriguez, Meritxell Bach Cuadra

Interestingly, the fractional anisotropy and, to a lesser extent, the mean diffusivity, are best recovered in missing slices by using the autoencoder trained with SH coefficients.


Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

no code implementations29 Jan 2022 Thomas Yu, Tom Hilbert, Gian Franco Piredda, Arun Joseph, Gabriele Bonanno, Salim Zenkhri, Patrick Omoumi, Meritxell Bach Cuadra, Erick Jorge Canales-Rodríguez, Tobias Kober, Jean-Philippe Thiran

In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability.

Anatomy Denoising +1

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues

no code implementations19 Jan 2022 Francesco La Rosa, Maxence Wynen, Omar Al-Louzi, Erin S Beck, Till Huelnhagen, Pietro Maggi, Jean-Philippe Thiran, Tobias Kober, Russell T Shinohara, Pascal Sati, Daniel S Reich, Cristina Granziera, Martina Absinta, Meritxell Bach Cuadra

Recently, advanced MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis.

Lesion Segmentation Specificity

An anatomically-informed 3D CNN for brain aneurysm classification with weak labels

no code implementations27 Nov 2020 Tommaso Di Noto, Guillaume Marie, Sébastien Tourbier, Yasser Alemán-Gómez, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi

We compare two strategies for negative patch sampling that have an increasing level of difficulty for the network and we show how this choice can strongly affect the results.

Binary Classification General Classification

Segmentation of the cortical plate in fetal brain MRI with a topological loss

no code implementations23 Oct 2020 Priscille de Dumast, Hamza Kebiri, Chirine Atat, Vincent Dunet, Mériam Koob, Meritxell Bach Cuadra

The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging.

Image Segmentation Semantic Segmentation

Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

no code implementations15 Aug 2020 Francesco La Rosa, Erin S Beck, Ahmed Abdulkadir, Jean-Philippe Thiran, Daniel S. Reich, Pascal Sati, Meritxell Bach Cuadra

The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention.

Lesion Detection

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

no code implementations10 Sep 2018 Francesco La Rosa, Mário João Fartaria, Tobias Kober, Jonas Richiardi, Cristina Granziera, Jean-Philippe Thiran, Meritxell Bach Cuadra

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients.

Lesion Segmentation

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