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OBJECTIVE Essential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard stereotactic deep brain stimulation or radiofrequency thalamotomy or, alternatively, minimally invasive techniques, including stereotactic rad ...

Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of qu ...

Cortical folding (gyrification) is determined during the first months of life, so that adverse events occurring during this period leave traces that will be identifiable at any age. As recently reviewed by Mangin and colleagues2, several methods exist to q ...

Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS), offering high diagnostic specificity and prognostic relevance. However, their routine clinical integration remains limited due to subtle magnetic resonance imaging (MRI) appearance, challenges in expert annotation, and a lack of standardized automated methods. We present a multi-centric comparative study of CL detection and segmentation in MRI. A total of 656 MRI scans, including clinical trial and research data from four

T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is challenging as fetal MRI acquisition relies on multiple motion-corrupted stacks of thick slices, requiring slice-to-volume reconstruction (SVR) to estimate a high-resolution (HR) 3D volume. Currently, T2 mapping involves repeated acquisitions of these stacks at each echo time (TE), leading to long scan times and high sensitivity to m

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty ...

Early identification of neurodevelopmental disorders relies on accurate segmentation of brain structures in infancy, a task complicated by rapid brain growth, poor tissue contrast, and motion artifacts in pediatric MRI. These challenges are further exacerbated in ultra-low-field (ULF, 0.064 T) MRI, which, despite its lower image quality, offers an affordable, portable, and sedation-free alternative for use in low-resource settings. In this work, we propose a domain randomization (DR) framework to bridge the

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution b

In this work we present a method for the image analysis of Magnetic Resonance Imaging (MRI) of fetuses. Our goal is to segment the brain surface from multiple volumes (axial, coronal and sagittal acquisitions) of a fetus. To this end we propose a two-step ...

OBJECTIVE The tremor circuitry has commonly been hypothesized to be driven by one or multiple pacemakers within the cerebello-thalamo-cortical pathway, including the cerebellum, contralateral motor thalamus, and primary motor cortex. However, previous stud ...

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to ...

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm ...

Accurate fetal brain segmentation is crucial for extracting biomarkers and assessing neurodevelopment, especially in conditions such as corpus callosum dysgenesis (CCD), which can induce drastic anatomical changes. However, the rarity of CCD severely limits annotated data, hindering the generalization of deep learning models. To address this, we propose a pathology-informed domain randomization strategy that embeds prior knowledge of CCD manifestations into a synthetic data generation pipeline. By simulatin

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, ...

Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of fetal brain MRI, an important processing step where multiple stacks of thick 2D slices are registered together and combined to build a single, isotropic

The thalamic nuclei are involved in many neurodegenerative diseases and therefore, their identification is of key importance in numerous clinical treatments. Automated segmentation of thalamic subparts is currently achieved by exploring diffusion-weighted ...

The “radiotherapy structure set” (RTSTRUCT) object of the DICOM standard is used for the transfer of patient structures and related data, between the devices found within and outside the radiotherapy department. It contains mainly the information for regions of interest (ROIs) and points of interest (e.g., dose reference points). In many cases, rather than manually drawing these ROIs on the CT images, one can indeed benefit from the wealth of automated segmentation algorithms available in ITK. But at presen

Among all satellite imaging systems, Synthetic Aperture Radar (SAR) is useful for monitoring purposes, as they provide data at any time under all weather conditions. Today, three spaceborne SAR systems are operational, while by the end of 2007 three furthe ...

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such ...

Objectives: Robust automated classification of paramagnetic rim lesions (PRLs) and remyelinated lesions based on iron and myelin content in people with multiple sclerosis (pwMS). Materials and Methods: In this prospective study (2018–2022), three-dimensional (3D) brain magnetic resonance imaging (MRI) from 180 pwMS (mean age: 47 ± 14 years, 108 females) were acquired at 3T including fluid-attenuated inversion recovery, magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE), and T2*-weighted se

Meritxell Bach Cuadra

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Pretherapeutic resting-state fMRI profiles are associated with MR signature volumes after stereotactic radiosurgical thalamotomy for essential tremor

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis

Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping

MBIS: Multivariate Bayesian Image Segmentation tool

Segmenting Infant Brains Across Magnetic Fields: Domain Randomization and Annotation Curation in Ultra-low Field MRI

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Brain Surface Segmentation of Magnetic Resonance Images of the Fetus

Normalization of aberrant pretherapeutic dynamic functional connectivity of extrastriate visual system in patients who underwent thalamotomy with stereotactic radiosurgery for essential tremor: a resting-state functional MRI study

Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization

Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains

Automatic Quality Control in Multi-centric Fetal Brain MRI Super-Resolution Reconstruction

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion-weighted magnetic resonance imaging

Exporting Contours to DICOM-RTSTRUCT

Time-Varying Segmentation for Mapping of Land Cover Changes

Dense Deformation Field Estimation for Atlas-based Segmentation of Pathological MR Brain Images

A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis