16 citations · 24 across the 7 of their papers we have counts for
5 papers · 1 filter
CorticalFlow: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability
Rodrigo Santa Cruz, Léo Lebrat, Darren Fu +4
The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurf…
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat +3
The study of neurodegenerative diseases relies on the reconstruction and analysis of the brain cortex from magnetic resonance imaging (MRI). Traditional frameworks for this task li…
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Vishnu M. Bashyam, Jimit Doshi, Guray Erus +24
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and…
Going deeper with brain morphometry using neural networks
Rodrigo Santa Cruz, Léo Lebrat, Pierrick Bourgeat +5
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convoluti…
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
Siyu Liu, Wei Dai, Craig Engstrom +4
Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to ex…