Finite element modeling is pivotal for understanding knee biomechanics, but its potential is often constrained by data availability. While the osteoarthritis initiative (OAI) provides invaluable imaging data, researchers still face the bottleneck of converting these images into usable simulations. [1] Existing benchmarks like OpenKnee are valuable, but restricted by unstructured meshes and low subject count (n=8) [2]. To address this, we present Kneeview, a repository of 81 patient-specific bone geometries (.stls) of the Femur, Tibia, Patella and Fibula. We are further expanding this resource to include structured Abaqus meshes, generated by morphing a comprehensive template (bone and soft tissue) to each patient's unique bone anatomy, thereby ensuring topological consistency across a variable population.

The dataset comprises of T2, fat saturated, knee MRI scans from 93 patients (38 male, 55 female) at Hospital Del Mar, Barcelona (2017-2024) using standard protocols. An nnUNet was trained on 35 expert annotations verified by a musculoskeletal-specialized radiologist. Removal of small island artifacts and exporting to .stl's was done using the built-in marching cubes of 3D Slicer. [3] In the final repository subjects with missing demographics were excluded and 81 final geometries were acquired. Structured mesh morphing will adjust the current available Bayesian Coherent Point Drift to contain object specific morphing for only bones while interpolating on the soft tissues, creating structured FEM meshes which will be added to the repository.

The nnUNet achieved high segmentation accuracy on a hold-out test dataset of 15 volumes with an average Dice coefficient of 0.986 ± 0.001 and IOU of 0.971 ± 0.002. The average Hausdorff Distance (HD95) was 14.464 ± 12.727. This variance was driven exclusively by distal tibial artefacts Tibia HD85: 16.49 ± 14.22) while Femur, Patella and Fibula gave an average HD95 of 1.46 ± 0.242.

Kneeview addresses the shortage of open-source simulation-ready knee geometries. The high Dice scores confirm that our automated pipeline produces geometries comparable to manual segmentation. Future work will include structured meshes for each of the 81 subjects such that this repository enables robust population-level finite element analysis.

Acknowledgements: This research study was co-funded by the European Union under a Horizon Europe MSCA Joint doctoral network with grant No.101169278, by the European Research Council (ERC-2021-CoG-O-Health-101044828), and by the Spanish Ministry of Science, Innovation, and Universities project STRATO - PID2021-126469OB-C21. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the REA can be held responsible for them.

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