MiniMAP: An automatic pipeline for paediatric lower limb musculoskeletal model generation
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The generation of paediatric musculoskeletal models often requires compromise between data availability, accuracy, and computation time. Statistical shape models allow for rapid generation of bone geometries for use in musculoskeletal modelling. An articulated shape model allows for movement at the joints during optimisation to better fit to experimental data. Here we present a methodology for generating custom paediatric OpenSim models using an articulated shape model of the lower limb generated from CT scans of over 300 children aged 4 to 18 years old. Bone geometries are predicted using demographic factors (age, height, mass, and sex), linear bone measurements (bone widths and lengths), and 12 skin surface landmarks (L/R ASIS, L/R PSIS, medial/lateral epicondyles, and medial/lateral malleoli). To account for skin padding (bone to skin surface distance) a random forest algorithm was developed for skin padding prediction for these 12 landmarks. Accuracy of the model prediction was assessed using a leave-one-out analysis to assess landmark errors, bone surface errors, and clinical bone measurement errors (linear measurements and angular values). Motion capture and MRI data were collected from two paediatric test cases to test the workflow on unseen data. Skin padding values varied widely in this population with average values from 3.4mm at the lateral malleolus to 25.9mm at the PSIS. Skin padding was best predicted using age, height, mass, ASIS width, epicondylar width, and malleolar width with an average RMSE of 4.68mm. The articulated shape model predicted accurate bone geometries from skin surface landmarks with average bone surface RMSEs of 2.64 ± 0.99mm, 1.91 ± 0.59mm, and 1.69 ± 0.56mm in the pelvis, femora, and tibiae/fibulae respectively. In the two unseen test cases, average bone surface errors ranged from 1.64 ± 1.2mm to 3.41 ± 3.1mm. This study demonstrated the accuracy of ‘MiniMAP’, for the rapid generation of custom paediatric musculoskeletal models.
Author Summary
Accurate modelling of the paediatric musculoskeletal system is important in applications including gait analysis, surgical planning, and rehabilitation. As large datasets in a paediatric population for any type of data are rare, therefore, compromises have to be made in the way both clinicians and researchers analyse motion capture data in a paediatric population. Often, this compromise will involve either a sacrifice in accuracy or a dramatic increase in computational time. For instance, we can approximate paediatric motion using an adult model. While this is a fast method, we know that there are differences when compared to a gold standard model. Alternatively, we can generate a fully subject specific model from medical imaging data, but this is time consuming and costly. Others prefer to use a mix of these two methods. Our research group has been focused on using statistical shape modelling as an alternative approach that allows for both rapid and accurate generation of paediatric musculoskeletal models. Presented here is the latest iteration of that work where we validate the performance of our open-source tools for generating custom models for clinicians and researchers alike.
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MiniMAP: An automatic pipeline for paediatric lower limb musculoskeletal model generation https://www.biorxiv.org/content/10.64898/2026.10.26.761767v1
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MiniMAP: An automatic pipeline for paediatric lower limb musculoskeletal model generation https://www.biorxiv.org/content/10.64898/2026.10.26.761767v1
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