Search Results for author: Léo Lebrat

Found 4 papers, 1 papers with code

CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability

no code implementations14 Jun 2022 Rodrigo Santa Cruz, Léo Lebrat, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption.

Surface Reconstruction

CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction

no code implementations6 Jun 2022 Léo Lebrat, Rodrigo Santa Cruz, Frédéric de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.

Surface Reconstruction

MongeNet: Efficient Sampler for Geometric Deep Learning

1 code implementation CVPR 2021 Léo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado

Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes.

Going deeper with brain morphometry using neural networks

no code implementations7 Sep 2020 Rodrigo Santa Cruz, Léo Lebrat, Pierrick Bourgeat, Vincent Doré, Jason Dowling, Jurgen Fripp, Clinton Fookes, Olivier Salvado

Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases.

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