63 citations · 291 across the 50 of their papers we have counts for
12 papers · 1 filter
BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-μm resolution for magnetic resonance methods development
Cristina Sainz Martinez, Meritxell Bach Cuadra, João Jorge
The benefits, opportunities and growing availability of ultra-high field magnetic resonance imaging (MRI) for humans have prompted an expansion in research and development efforts…
Self-Supervised Isotropic Superresolution Fetal Brain MRI
Kay Lächler, Hélène Lajous, Michael Unser +2
Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thi…
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
Nataliia Molchanova, Vatsal Raina, Andrey Malinin +6
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause…
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
Priscille de Dumast, Thomas Sanchez, Hélène Lajous +1
Tuning the regularization hyperparameter in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, w…
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection
Tommaso Di Noto, Meritxell Bach Cuadra, Chirine Atat +5
Gliomas are the most frequent primary brain tumors in adults. Glioma change detection aims at finding the relevant parts of the image that change over time. Although Deep Learning…
Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI
Athena Taymourtash, Hamza Kebiri, Ernst Schwartz +6
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and exc…
Slice estimation in diffusion MRI of neonatal and fetal brains in image and spherical harmonics domains using autoencoders
Hamza Kebiri, Gabriel Girard, Yasser Aleman-Gomez +4
Diffusion MRI (dMRI) of the developing brain can provide valuable insights into the white matter development. However, slice thickness in fetal dMRI is typically high (i.e., 3-5 mm…
Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI
Priscille de Dumast, Hamza Kebiri, Vincent Dunet +2
The fetal cortical plate (CP) undergoes drastic morphological changes during the in utero development. Therefore, CP growth and folding patterns are key indicator in the assessment…
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic +15
Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
Francesco La Rosa, Maxence Wynen, Omar Al-Louzi +11
The current multiple sclerosis (MS) diagnostic criteria lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, c…
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
Thomas Yu, Tom Hilbert, Gian Franco Piredda +8
Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampl…