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Nathan Molinier

nathan.molinier@mila.quebec
PhD - Polytechnique Montréal
Supervisor
Julien Cohen-Adad
Research Topics
Computer Vision
Deep Learning
Medical Machine Learning
GitHub

Publications

VERIDAH: solving enumeration anomaly–aware vertebra labeling across imaging sequences
Hendrik Möller
Hanna Schoen
Robert Graf
Matan Atad
Nathan Molinier
Anjany Sekuboyina
Bettina K. Budai
Fabian Bamberg
Steffen Ringhof
Christopher Schlett
Tobias Pischon
Thoralf Niendorf
Josua A. Decker
Marc-André Weber
Bjoern Menze
Daniel Rueckert
Jan S. Kirschke
OBJECTIVES: Although the identification of enumeration anomalies has potential clinical implications for chronic back pain and operation pla… (see more)nning, the thoracolumbar junction is poorly assessed and rarely described in clinical reports. Additionally, even though multiple deep-learning-based vertebra labeling algorithms exist, there is a lack of methods to automatically label enumeration anomalies. MATERIALS AND METHODS: We introduce "Vertebra Identification with Anomaly Handling" (VERIDAH), a novel vertebra labeling algorithm based on multiple classification heads combined with a weighted vertebra sequence prediction algorithm. We use the NAKO dataset for MRI and a CT cohort to retrospectively evaluate the labeling performance by computing how often the model is correct in predicting the vertebra labels, including enumeration anomalies. Statistical significance was assessed using the Wilcoxon signed-rank test. RESULTS: On 1000 T2w sagittal (mean age 52 ± 11, 52% female) and 365 CT (mean age 74 ± 21, 44% female) images, we show that our approach surpasses existing models on T2w TSE sagittal (98.30% vs. 94.24% of subjects with all vertebrae correctly labeled, p 0.01) and CT imaging (99.18% vs. 77.26% of subjects with all vertebrae correctly labeled, p 0.01) and works in arbitrary field-of-view images. VERIDAH correctly labeled the presence of thoracic enumeration anomalies in 87.80% and 96.30% of T2w and CT images, respectively, and lumbar enumeration anomalies in 94.48% and 97.22% for T2w and CT, respectively. CONCLUSION: VERIDAH offers enumeration anomaly robust vertebra labeling in CT and T2w sagittal imaging and is publicly released along with a spine segmentation model. KEY POINTS: QuestionAutomatically identifying enumeration anomalies in the spine is important in determining the correct vertebra labels, especially in limited field-of-view imaging. FindingsA carefully designed classifier with multiple classification heads and a sequence predictor provides superior results on large benchmarks, both in CT and MRI. Clinical relevanceThe proposed method identifies vertebra levels even in very limited field-of-view imaging, and correctly predicts enumeration anomalies, making downstream tasks such as surgery planning easier. Its public availability enables studies and applications to take these spinal anomalies into account.
2026-10-15
European Radiology (published)
doi.org
arxiv.org
Structural Remodeling of the Lumbar Spine Following Non-Surgical Spinal Decompression: An Automated MRI Study
Jacob A. Connolly
Nathan Molinier
Kenneth A. Weber
Julien Cohen‐Adad
Nathan D. Schilaty
Background and Objectives: To investigate MRI changes using SpineReport (r20260928) following non-surgical spinal decompression (NSSD) in pa… (see more)tients with chronic low back pain (cLBP). We conducted a retrospective review of patients treated with NSSD who obtained MRI within 6 months of treatment initiation. Methods: Thirty-six subjects (20 F, 16 M), with a mean age of 55.9 ± 16.4 years, were treated at the USF Health Chiropractic Clinic, Department of Neurosurgery, Brain & Spine. NSSD using DRX9000® (Excite Medical, Tampa, FL, USA) was utilized, with a median of 20 (20, 25) treatments. Treatment levels included L3 (n = 2), L4 (n = 18), and L5 (n = 16). Sagittal T2-weighted images were processed using TotalSpineSeg (r20251124) for automated segmentation, with SpineReport used to quantify disc, vertebrae, spinal canal, and intervertebral foramen metrics. Results: Mean pain scores improved by 3.4 ± 1.8 from 5.6 ± 1.6 (p 0.001), while Oswestry Disability Index scores improved by 10.8 ± 9.1 from 17.3 ± 8.0 (p 0.001). At the treated level, spinal canal cross-sectional area increased from 170.9 to 175.8 mm2 (2.9%, p = 0.040), and right–left diameter increased from 16.0 to 16.3 mm (1.9%, p = 0.025). Disc volume decreased from 7762.8 to 7449.5 mm3 (−4.0%, p = 0.046). No significant changes were observed in disc thickness, DHI, intervertebral foramen area, or vertebral measurements (p > 0.05). Conclusions: SpineReport enabled automated quantification of key spinal structures. NSSD was associated with increased spinal canal dimensions and improved symptoms. Further research is needed to determine the persistence and clinical significance of these structural changes.
2026-09-28
Journal of Functional Morphology and Kinesiology (published)
doi.org
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… (see more)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 (preprint)
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… (see more)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 (preprint)
doi.org
arxiv.org