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Nathan Molinier

nathan.molinier@mila.quebec
Doctorat - Polytechnique
Superviseur⋅e principal⋅e
Julien Cohen-Adad
Sujets de recherche
Apprentissage automatique médical
Apprentissage profond
Vision par ordinateur
GitHub

Publications

SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI
Nathan Molinier
Adrian A. Marth
Reto Sutter
Christoph Germann
Jacob A. Connolly
Mathieu Guay-Paquet
Nathan D. Schilaty
Kenneth A. Weber II
Julien Cohen-Adad
Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challengin… (voir plus)g. In clinical practice, analysis is predominantly performed in two dimensions (2D), as manual three-dimensional (3D) assessment is time-consuming. However, 2D measurements suffer from limited reproducibility, particularly when anatomical structures are not aligned with the imaging plane. Existing automated approaches are often restricted to 2D, rely on discrete grading, or lack robustness and interpretability. We introduce SpineReport, an open-source, fully automated framework for comprehensive 3D morphometric analysis of lumbar spine MRI. Leveraging robust anatomical segmentations, the method extracts quantitative metrics from key structures, including the spinal canal, spinal cord, vertebrae, intervertebral discs, and foramina. These include both morphological and signal-based features, enabling cross-subject and longitudinal assessment. SpineReport further generates subject-specific reports that allow comparison with cohort distributions, improving interpretability and objective characterization of spinal morphology. Clinical relevance was evaluated against radiologist-reported severity grades for central canal, lateral recess, and foraminal stenosis. Metrics showed strong associations with central canal stenosis severity, with T2-weighted CSF signal providing the highest performance (AUC = 0.95). Canal AP diameter and area ratios also demonstrated strong correlations and high discriminative ability (AUC>0.80). For lateral recess stenosis, associations were moderate, with lateral CSF signal being the most informative (AUC = 0.73). No significant associations were observed for foraminal stenosis despite robust region-of-interest extraction. SpineReport is released as an open-access tool: https://ivadomed.github.io/SpineReport/
2026-06-07
arXiv (prépublication)
doi.org
arxiv.org
One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
Nathan Molinier
Hendrik Möller
Thomas Dagonneau
Anna Curto-Vilalta
Robert Graf
Matan Atad
Daniel Rueckert
Jan S. Kirschke
Julien Cohen-Adad
Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. Howe… (voir plus)ver, model performance remains limited by the scarcity of high-quality annotated data and insufficient generalization across imaging protocols. This limitation is particularly evident in MRI and CT, where models are typically trained on a single acquisition sequence and exhibit reduced robustness when applied to unseen sequences or contrasts. Although data augmentation is widely used to improve general robustness on medical images, its impact on cross-modality generalization has not been quantitatively explored. In this work, we study a targeted set of data augmentation techniques designed to improve cross-modality transfer. We train three spine segmentation models, each on a single-modality/sequence dataset, and evaluate them across seven out-of-distribution datasets (spanning CT and MRI), reflecting a realistic single-sequence training and multi-sequence/contrast/modality deployment scenario. Our results demonstrate substantial performance gains on unseen domains (average Dice gain of 155 %) while preserving in-domain accuracy (average Dice decrease of 0.008 %), including effective transfer between CT and MRI. To mitigate the computational cost typically associated with strong data augmentation, we implement GPU-optimized augmentations that maintain, and even improve, training efficiency by approximately 10 %. We release our approach as an open-source toolbox, enabling seamless integration into commonly used frameworks such as nnUNet and MONAI. These augmentations significantly enhance robustness to heterogeneous clinical imaging scenarios without compromising training speed.
2026-05-03
arXiv (prépublication)
doi.org
arxiv.org