6 citations · 16 across the 5 of their papers we have counts for
5 papers
CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans
Sergio Morell-Ortega, Ángela González-Cebrián, Boris Mansencal +8
Large-scale automated morphometric analysis of brain MRI is limited by the thick-slice, anisotropic acquisitions prevalent in routine clinical practice. Existing generative super-r…
DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI
Sergio Morell-Ortega, Marina Ruiz-Perez, Marien Gadea +7
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution ($1 \text{…
DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI
Marina Ruiz-Perez, Sergio Morell-Ortega, Marien Gadea +7
The implication of the thalamus in multiple neurological pathologies makes it a structure of interest for volumetric analysis. In the present work, we have designed and implemented…
Medical imaging data structure extended to multiple modalities and anatomical regions
J. M. Saborit-Torres, J. J. Saenz-Gamboa, J. À. Montell +13
Brain Imaging Data Structure (BIDS) allows the user to organise brain imaging data into a clear and easy standard directory structure. BIDS is widely supported by the scientific co…
Deep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction
José V. Manjón, Jose E. Romero, Roberto Vivo-Hernando +4
Automatic methods for measuring normalized regional brain volumes from MRI data are a key tool to help in the objective diagnostic and follow-up of many neurological diseases. To e…