Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
601-02-005 ISMRM Abstract

MuscleMap Toolbox: Open-Source, Contrast-Agnostic Tools for Automated Whole-Body Muscle Segmentation and 3D Quantification

Primary: Musculoskeletal - Muscle
Secondary: Musculoskeletal - Bone
601-02-005 · Muscle MRI in Health and Disease: From Standardization to Application · Thursday, 14 May, 1:40 PM–3:30 PM · Hall 1A
Keywords: Bone MSK Muscle Musculoskeletal MRI Computed Tomography
Accepted
Evert Onno WEsselink1, James M Elliott2,3, Marnee J McKay4, Ananya Goyal5, Dario Pfyffer1,6, Richard Yin1, Sandrine Bédard7, Christine S W Law1, Brian Kim2,3, Julien Cohen-Adad8,9,10,11, Benjamin De Leener8,12,13, Kenneth Weber 1
1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford Medicine, Stanford, United States of America
2The Kolling Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
3Northern Sydney Local Health District, The University of Sydney, Sydney, Australia
4Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
5Department of Radiology, Stanford University, Stanford, United States of America
6Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford Medicine, Stanford, United States of America
7NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
8NeuroPoly Lab, Montreal, Canada
9Mila - Quebec AI Institute, Montreal, Canada
10Functional Neuroimaging Unit, Montreal, Canada
11Centre de Recherche du CHU Sainte-Justine, Montreal, Canada
12Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, Canada
13CHU Sainte-Justine Research Center, Montréal, Canada, Canada
Presenting Author: Kenneth Weber

Synopsis

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References

1. Wesselink EO, Pool-Goudzwaard A, De Leener B, et al. Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: A UK Biobank study. Spine J 2024; published online Feb. doi:10.1016/j.spinee.2024.02.013. [doi]
2. Elliott JM, Hancock MJ, Crawford RJ, Smith AC, Walton DM. Advancing imaging technologies for patients with spinal pain: with a focus on whiplash injury. Spine J 2018; 18: 1489–97. doi:10.1016/j.spinee.2017.06.015 [doi]
3. Rolland Y, Czerwinski S, van Kan GA, et al. Sarcopenia: Its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Heal aging 2008; 12: 433–50. doi:10.1007/BF02982704 [doi]
4. Stephens NA, Skipworth RJE, MacDonald AJ, Greig CA, Ross JA, Fearon KCH. Intramyocellular lipid droplets increase with progression of cachexia in cancer patients. J Cachexia Sarcopenia Muscle 2011; 2: doi:111–7.10.1007/s13539-011-0030-x
5. Wesselink EO, Verheijen E, Djuric N, Coppieters M, Elliott J, Weber KA 2nd, Wouter M, Vleggeert-Lankamp C, Pool-Goudzwaard A. Lumbar Multifidus Intramuscular fat Concentrations are Associated With Recovery Following Decompressive Surgery for Lumbar Spinal Stenosis. A Longitudinal Cohort Study With 5-year Follow-up. Spine (Phila Pa 1976). 2025 May 27:10.1097/BRS.0000000000005408. doi: 10.1097/BRS.0000000000005408. [doi]
6. Weber KA, Smith AC, Wasielewski M, et al. Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury. Sci Rep 2019; 9: 7973. doi:10.1038/s41598-019-44416-8 [doi]
7. Wesselink EO, Elliott JM, Coppieters MW, et al. Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain. Sci Rep 2022; 12: 13485. doi:10.1038/s41598-022-16710-5 [doi]
8. Perraton Z, Mosler AB, Lawrenson PR, et al. The association between lateral hip muscle size/intramuscular fat infiltration and hip strength in active young adults with long standing hip/groin pain. Phys Ther Sport 2024; 65: 95–101. doi: 10.1016/j.ptsp.2023.11.007 [doi]
9. McKay MJ, Weber KA, Wesselink EO, et al. MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle. J Imaging 2024; 10: 262. doi: 10.3390/jimaging10110262 [doi]
10. Kim B, Gandomkar Z, McKay MJ, et al. Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis. J shoulder Elb Surg 2025; 34: 2224–38. doi: 10.1016/j.jse.2024.12.033 [doi]
11. Cardoso MJ, Li W, Brown R, et al. MONAI: An open-source framework for deep learning in healthcare. 2022. doi:10.48550/arXiv.2211.02701. [doi]
12. Wesselink EO, Elliott JM, Pool-Goudzwaard A, et al. Quantifying lumbar paraspinal intramuscular fat: Accuracy and reliability of automated thresholding models. North Am Spine Soc J 2024; 17: doi: 10.1016/j.xnsj.2024.100313 [doi]
13. Weber KA, Wesselink EO, Gutierrez J, et al. Three-dimensional spatial distribution of lumbar paraspinal intramuscular fat revealed by spatial parametric mapping. Eur Spine J 2025; 34: 27–3. doi: 10.1007/s00586-024-08559-1 [doi]

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http://echo.ismrm.org/p/ISMRM2026/601-02-005