Hervé Lombaert

zeinab.abboud@mila.quebec

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

pierre-louis.benveniste@mila.quebec

Pierre Louis Benveniste

PhD - Polytechnique Montréal

Principal supervisor :

Julien Cohen-Adad

Website

victoria-mae.carriere@mila.quebec

Victoria-Mae Carrière

Research Intern - Polytechnique Montréal

Florence Dell'Aniello-Picard

PhD - Polytechnique Montréal

florence.dellaniello-picard@mila.quebec

melanie.gaillochet@mila.quebec

PhD - Polytechnique Montréal

Website

sofia.loredo-hernandez@mila.quebec

Tibor Kubik

PhD - Polytechnique Montréal

tibor.kubik@mila.quebec

Sofía Loredo Hernández

Master's Research - Polytechnique Montréal

pranav.poudel@mila.quebec

Pranav Poudel

PhD - Polytechnique Montréal

nairouz.shehata@mila.quebec

Postdoctorate - Polytechnique Montréal

matei.voillemier@mila.quebec

Matei Voillemier

Master's Research - Polytechnique Montréal

Publications

Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction

Pranav Poudel

Florence Dell’Aniello Picard

Frédéric Lavoie

Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray pro… (see more)jections is severely ill-posed. Recently, 3D Gaussian Splatting has achieved state-of-the-art performance in sparse-view reconstruction by representing the volume using explicit, optimized primitives, but it requires dozens of projected views. With fewer views, reconstruction quality degrades severely since the explicit primitives are optimized freely without any anatomical information. Anatomical structures, in contrast, share similar geometry and density across a population. Their variations are bounded within a limited range that statistical shape models can capture. This paper proposes a shape-guided Gaussian splatting framework for sparse-view X-ray 3D reconstructions. Our contribution lies in driving Gaussian positions toward anatomically valid configurations, alongside atlas-based density regularization. Our method ensures anatomically consistent reconstruction and improves PSNR by 2.83 dB over a state-of-the-art Gaussian splatting baseline with as few as 5 views. Code Available: https://github.com/polyshape-lab/ShapeGuidedGaussian

2026-09-08

arXiv (preprint)

A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction

Florence Dell’Aniello Picard

Pranav Poudel

Frédéric Lavoie

Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but rema… (see more)ins ill-posed as X-ray projections discard depth information. Existing methods often incorporate a 3D statistical shape model (SSM) as a shape prior to guide reconstructions toward anatomically plausible shapes, relying on iterative 3D-to-2D projection matching. Yet, these approaches are computationally expensive and constrain their SSM to a single dimensionality, leaving the statistical relationship between 2D observations and 3D geometry largely unexploited and unexplored. We instead propose a joint 2D-3D SSM that explicitly captures the co-variation between 2D and 3D segmentations in a shared latent space. During training, 2D and 3D segmentations are registered to a common 3D template and its corresponding 2D projections, and the resulting stationary velocity fields are jointly decomposed using principal component analysis (PCA). This joint modeling allows the 2D-to-3D mapping to be learned directly from data rather than computing correspondences at inference time. For unseen subjects, the 3D shape is recovered directly by lifting the 2D latent coordinates to the 3D PCA subspace, thereby eliminating the need for iterative 3D-to-2D projection. Experiments on NMDID demonstrate that the proposed joint 2D-3D SSM outperforms a widely-used 3D-only SSM baseline while achieving inference approximately 4 times faster, at under 3 seconds per subject. The code is available at: https://github.com/florence-dellaniello-picard/joint2d3d-ssm.

2026-09-07

arXiv (preprint)

Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study

Pierre-Louis Benveniste

Julian McGinnis

Shannon Kolind

Larry D. Lynd

Sarah A. Morrow

Jiwon Oh

Alexandre Prat

Alice Schabas

Penelope Smyth

Roger Tam

Anthony Traboulsee

Mark Mühlau

Julien Cohen-Adad

Longitudinal characterization of multiple sclerosis (MS) lesions remains constrained by the lack of frameworks capable of establishing consi… (see more)stent instance-level correspondences across time. Conventional segmentation approaches produce semantic lesion masks at each visit and therefore fail to capture the complex instance temporal patterns associated with lesion appearance, disappearance, splitting, or merging. This study presents a comparative evaluation of five strategies for automated tracking of spinal cord MS lesions in longitudinal MRI data from a multi-site cohort. The investigated strategies rely either on deformable registration or on a spinal anatomical reference system, and encompass overlap-based matching, coordinate-based Hungarian algorithm, gradient-boosted classification, and Siamese model classification. Tracking accuracy is quantified using instance-level true positives, false positives, and false negatives, allowing to assess the presence of one-to-many and many-to-one associations. Results show best performance for the registration-based overlap method. This study provides the first systematic analysis of lesion-instance correspondence in the spinal cord and outlines the strengths and limitations of registration-based and registration-free paradigms for longitudinal MS assessment. The code is available at http://github.com/ivadomed/longitudinal-sc-ms-lesion-tracking .

2026-09-07

arXiv (preprint)

Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

Dexter Wen Jie Teo

Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Qu… (see more)antifying these dynamic structural changes across the cardiac cycle is essential for measuring aortic distensibility, a primary marker of arterial stiffness. However, standard 2D segmentation networks focus on each frame independently. Consequently, when rapid systolic flow temporarily obscures the aorta's boundaries, this lack of continuous context results in frame-to-frame tracking dropouts and boundary inconsistencies. Spatiotemporal (

2026-08-23

arXiv (preprint)

Deep Spectral Models for Robust Dental Shape Generation

Tibor Kubik

François Guibault

Michal Španěl

Accurate modeling of dental crown morphology is fundamental for diagnosis, orthodontic planning, and computer-aided restoration design. Howe… (see more)ver, datasets suitable for training such models are typically limited in size. We present ToothForge, a deep spectral generative framework that models dental crown geometries from compact, intrinsic representations. By operating in the spectral domain, ToothForge learns a latent manifold of 3D tooth shapes through synchronized spectral embeddings, ensuring consistent modeling across samples with varying connectivity. Spectral synchronization mitigates the instability of Laplace-Beltrami eigenbases and enables efficient learning in a low-dimensional space. The framework is thoroughly evaluated through robustness analysis, ablation studies, and benchmarking against PCA-based statistical shape models and point-based generative frameworks. Results show that synchronized spectral modeling achieves reconstruction and generative performance comparable to or exceeding spatial approaches, while maintaining compactness and geometric interpretability. Together, the compact synchronized coefficients and low-dimensional learning space make the framework particularly suitable for limited datasets, as often encountered in dental and medical domains, and applicable in real-world scenarios where guaranteeing consistent connectivity across shapes from various clinics is unrealistic.

2026-06-28

Machine Learning for Biomedical Imaging (published)

Exploring Entropy-based Active Learning for Fair Brain Segmentation

Ghazal Danaee

Christian Desrosiers

Sylvain Bouix

Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. Howeve… (see more)r, standard uncertainty-based AL methods typically focus on maximizing performance metrics, ignoring performance disparities or fairness across groups with sensitive attributes. While fair active learning has been explored in classification tasks, its intersection with medical image segmentation remains unaddressed. In this work, we introduced a fairness-aware active learning framework with a Weighted Entropy selection strategy that modulates uncertainty based on current group-specific performance estimates on the labeled set. To decouple true epistemic uncertainty from anatomical volume variances, we further utilized a masked, scaled entropy restricted to the region of interest. The framework was evaluated on synthetic T1-weighted brain MRIs with controlled left caudate bias in both strong and weak bias settings. A 3D U-Net was trained to segment the left caudate under several AL strategies, starting from both demographically balanced and strongly imbalanced initial labeled sets. Experiments demonstrated that our method markedly reduces performance disparities between groups compared to random sampling and standard uncertainty sampling. By prioritizing poorly segmented subgroups during the AL cycles, our method consistently achieved the highest equity-scaled performance and reduced the disparity metric by 75% (strong bias) and 86% (weak bias) relative to standard entropy at the final budget. Overall, this work is among the first studies on fair AL for medical image segmentation, offering an efficient strategy to train more equitable models in resource-constrained environments.

2026-05-04

Medical Imaging with Deep Learning (published)

proceedings.mlr.press

Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers

Pierre‐Louis Benveniste

Laurent Létourneau‐Guillon

David Araújo

Lydia Chougar

Dumitru Fetco

Masaaki Hori

Kouhei Kamiya

Steven Messina

Charidimos Tsagkas

Bertrand Audoin

Rohit Bakshi

Élise Bannier

Daniel Blezek

Jean‐Christophe Brisset

Virginie Callot

Erik Charlson

Michelle Chen

Olga Ciccarelli

Sarah Demortière

Gilles Edan … (see 36 more)

M Filippi

Tobias Granberg

Cristina Granziera

Christopher C. Hemond

B. Mark Keegan

Anne Kerbrat

J Kirschke

Petr Kudlička

Pierre Labauge

Lisa Eunyoung Lee

Yaou Liu

Caterina Mainero

Julian McGinnis

Nilser Laines Medina

Mark Mühlau

Govind Nair

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Alexandre Prat

Daniel S. Reich

Maria A. Rocca

Timothy M. Shepherd

Seth A. Smith

Leszek Stawiarz

Jason Talbott

Roger Tam

Shahamat Tauhid

Anthony Traboulsee

Constantina A. Treaba

Paola Valsasina

Zachary Vavasour

Marios Yiannakas

Julien Cohen-Adad

Shannon Kolind

The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barr… (see more)ier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher.Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast.

2026-04-23

Multiple Sclerosis Journal (published)

Anatomically-aware conformal prediction for medical image segmentation with random walks

Christian Desrosiers

2026-01-25

ArXiv (preprint)

Preface

Tal Arbel

Ismail Ben Ayed

Marleen de Bruijne

Maxime Descoteaux

Christopher Pal

2026-01-06

Acta Neerlandica (published)

proceedings.mlr.press

Prompt learning with bounding box constraints for medical image segmentation.

Mehrdad Noori

Sahar Dastani

Christian Desrosiers

Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised app… (see more)roaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing user intervention. However, existing prompt learning approaches depend on fully annotated segmentation masks. This paper proposes a novel framework that combines the representational power of foundation models with the annotation efficiency of weakly supervised segmentation. More specifically, our approach automates prompt generation for foundation models using only bounding box annotations. Our proposed optimization scheme integrates multiple constraints derived from box annotations with pseudo-labels generated by the prompted foundation model. Extensive experiments across multi-modal datasets reveal that our weakly supervised method achieves an average Dice score of 84.90% in a limited data setting, outperforming existing fully-supervised and weakly-supervised approaches. The code will be available upon acceptance

2025-12-31

IEEE Trans. Biomed. Eng. (published)

TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses

Sahar Dastani

Ali Bahri

Gustavo Adolf Vargas Hakim

Moslem Yazdanpanah

Mehrdad Noori

David Osowiechi

Samuel Barbeau

Ismail Ben Ayed

Christian Desrosiers

State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering arc… (see more)hitecture designed for vision tasks. However, their generalization performance degrades significantly under distribution shifts. To address this limitation, we propose TRUST (Test-Time Refinement using Uncertainty-Guided SSM Traverses), a novel test-time adaptation (TTA) method that leverages diverse traversal permutations to generate multiple causal perspectives of the input image. Model predictions serve as pseudo-labels to guide updates of the Mamba-specific parameters, and the adapted weights are averaged to integrate the learned information across traversal scans. Altogether, TRUST is the first approach that explicitly leverages the unique architectural properties of SSMs for adaptation. Experiments on seven benchmarks show that TRUST consistently improves robustness and outperforms existing TTA methods.

2025-12-02

Conference on Neural Information Processing Systems (Accept (poster))

openreview.net

Anatomically-Focused Patches for Lightweight and Explainable Knee OA Grading

Tien-en Chang

2025-10-04

Lecture Notes in Computer Science (published)