RegQCNET: Deep Quality Control for Image-to-template Brain MRI Affine Registration

no code implementations • 14 May 2020 • Baudouin Denis de Senneville, José V. Manjon, Pierrick Coupé

In the current study, a compact 3D convolutional neural network (CNN), referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template.

Brain Segmentation

AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation

no code implementations • 20 Nov 2019 • Pierrick Coupé, Boris Mansencal, Michaël Clément, Rémi Giraud, Baudouin Denis de Senneville, Vinh-Thong Ta, Vincent Lepetit, José V. Manjon

Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.

Brain Segmentation Decision Making +1

AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

no code implementations • 5 Jun 2019 • Pierrick Coupé, Boris Mansencal, Michaël Clément, Rémi Giraud, Baudouin Denis de Senneville, Vinh-Thong Ta, Vincent Lepetit, José V. Manjon

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.

Brain Segmentation Decision Making +1