83 citations · 141 across the 10 of their papers we have counts for
6 papers · 1 filter
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
MRI denoising using Deep Learning and Non-local averaging
Jose V. Manjon, Pierrick Coupe
This paper proposes a novel method for automatic MRI denoising that exploits last advances in deep learning feature regression and self-similarity properties of the MR images. The…
Multi-scale Graph-based Grading for Alzheimer's Disease Prediction
Kilian Hett, Vinh-Thong Ta, José V. Manjón +1
The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on…
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Rémi Giraud, Vinh-Thong Ta, Aurélie Bugeau +2
Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non…
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis +4
Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated sta…