Patrick Pessaux

According to our database, Patrick Pessaux authored at least 7 papers between 2015 and 2019.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2019

A human liver cell atlas reveals heterogeneity and epithelial progenitors.

Nadim Aizarani, Antonio Saviano, Sagar, Laurent Mailly, Sarah Durand, Josip S. Herman, Patrick Pessaux, Thomas F. Baumert, Dominic Grün

Nat., 2019

Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

Farid Ouhmich, Vincent Agnus, Vincent Noblet, Fabrice Heitz, Patrick Pessaux

Int. J. Comput. Assist. Radiol. Surg., 2019

2018

Robust Supervoxel Matching Combining Mid-Level Spectral and Context-Rich Features.

Florian Tilquin, Pierre-Henri Conze, Patrick Pessaux, Mathieu Lamard, Gwenolé Quellec, Vincent Noblet, Fabrice Heitz

Proceedings of the Patch-Based Techniques in Medical Imaging, 2018

2017

Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans.

Pierre-Henri Conze, Vincent Noblet, François Rousseau, Fabrice Heitz, Vito de Blasi, Riccardo Memeo, Patrick Pessaux

Int. J. Comput. Assist. Radiol. Surg., 2017

Hierarchical multi-scale supervoxel matching using random forests for automatic semi-dense abdominal image registration.

Pierre-Henri Conze, Florian Tilquin, Vincent Noblet, François Rousseau, Fabrice Heitz, Patrick Pessaux

Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017

2016

Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans.

Pierre-Henri Conze, Vincent Noblet, François Rousseau, Fabrice Heitz, Riccardo Memeo, Patrick Pessaux

Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, 2016

2015

Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels.

Pierre-Henri Conze, François Rousseau, Vincent Noblet, Fabrice Heitz, Riccardo Memeo, Patrick Pessaux

Proceedings of the Machine Learning in Medical Imaging - 6th International Workshop, 2015