Portrait of Hervé Lombaert

Hervé Lombaert

Associate Academic Member
Associate Professor, Polytechnique Montréal, Department of Computer Engineering Department
Research Topics
Computer Vision
Learning on Graphs
Medical Machine Learning
Google Scholar

Biography

Hervé Lombaert is an associate professor in the Computer Engineering Department at Polytechnique Montréal, and the Canada Research Chair in Shape Analysis in Medical Imaging. His research focuses on the statistics and analysis of shapes in the context of machine learning and medical imaging. His work on graph analysis has impacted several applications in medical imaging, from early image segmentation with graph-cuts to recent surface analysis with spectral graph theory.

Lombaert has authored over seventy papers, holds five patents and has earned several awards, including the IPMI Erbsmann Prize. His students have also received best thesis awards with impactful publications in medical image computing. He is on the editorial board of Medical Image Analysis. He has also worked in a number of other research centres, including the INRIA Sophia-Antipolis (France), Microsoft Research (Cambridge, U.K.), Siemens Corporate Research (Princeton, NJ) and McGill University.

Current Students

Zeinab Abboud
PhD - Polytechnique Montréal
Github
Sukesh Adiga Vasudeva
PhD - Polytechnique Montréal
Website
Github
Google Scholar
Pierre Louis Benveniste
PhD - Polytechnique Montréal
Principal supervisor :
Julien Cohen-Adad
Website
Github
Google Scholar
Sahar Dastani Oghani
PhD - École de technologie suprérieure
Website
Github
Melanie Gaillochet
PhD - Polytechnique Montréal
Github
Pranav Poudel
PhD - Polytechnique Montréal
Matei Voillemier
Master's Research - Polytechnique Montréal

Publications

Medial Spectral Coordinates for 3D Shape Analysis
Morteza Rezanejad
Mohammad Khodadad
H. Mahyar
Hervé Lombaert
M. Gruninger
Dirk. B. Walther
Kaleem Siddiqi
In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects repre-sented by surface meshes,… (see more) their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral co-ordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to iso-metric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence.
2021-11-26
ArXiv (preprint)
doi.org
arxiv.org
Preface
Tal Arbel
Ismail Ben Ayed
Marleen de Bruijne
Maxime Descoteaux
Hervé Lombaert
Chris Pal
2020-09-21
Proceedings of the Third Conference on Medical Imaging with Deep Learning (published)
proceedings.mlr.press
Medical Imaging with Deep Learning: MIDL 2020 - Short Paper Track
Tal Arbel
Ismail Ben Ayed
Marleen de Bruijne
Maxime Descoteaux
Hervé Lombaert
Chris Pal
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (M… (see more)IDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Research (PMLR).
2020-06-29
ArXiv (preprint)
arxiv.org
arxiv.org