Search Results for author:
Found 35 papers, 14 papers with code
Maximizing domain generalization in fetal brain tissue segmentation: the role of synthetic data generation, intensity clustering and real image fine-tuning
In this work, we investigate how to maximize the out-of-domain (OOD) generalization potential of SynthSeg-based methods in fetal brain MRI.
Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo.
Exploiting XAI maps to improve MS lesion segmentation and detection in MRI
To date, several methods have been developed to explain deep learning algorithms for classification tasks.
Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation.
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Saliency maps (based on SmoothGrad) in FLAIR showed positive values inside a lesion and negative in its neighborhood.
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains
However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages.
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
The results from a multi-centric MRI dataset of 444 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales compared to measures that average voxel-scale uncertainty values.
FetMRQC: a robust quality control system for multi-centric fetal brain MRI
We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data.
Direct segmentation of brain white matter tracts in diffusion MRI
The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.
Robust thalamic nuclei segmentation from T1-weighted MRI
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
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies.
Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation
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.
Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation
Quantitative analysis of in utero human brain development is crucial for abnormal characterization.
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
Tuning the regularization hyperparameter $\alpha$ in inverse problems has been a longstanding problem.
Self-Supervised Isotropic Superresolution Fetal Brain MRI
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).
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI).
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection
The weak labels extracted from radiology reports allowed us to increase dataset size more than 3-fold, and improve VGG classification results from 75% to 82% AUC.
Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI
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
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.
Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI
Therefore, CP growth and folding patterns are key indicator in the assessment of the brain development and maturation.
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
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.
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
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.
4D iterative reconstruction of brain fMRI in the moving fetus
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero.
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation
The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment.
FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life.
Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge
Here, we present a DL model for aneurysm detection that overcomes the issue with ''weak'' labels: oversized annotations which are considerably faster to create.
An anatomically-informed 3D CNN for brain aneurysm classification with weak labels
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.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
Segmentation of the cortical plate in fetal brain MRI with a topological loss
The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging.
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
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.
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
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.
