ArticleFrontiers in bioengineering and biotechnology2021
Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Statistical shape modelling of the human lumbar spine: a scoping review characterising the research landscape and potential clinical applications.Frontiers in bioengineering and biotechnology · 2026Pooled it
- A Review of Motion-Preserving Cervical Spinal Implants and Fusion Constructs.Bioengineering (Basel, Switzerland) · 2026Review
- A pipeline to morph finite element models of the lumbar spine for generation of customised spinal cages.Journal of orthopaedic surgery and research · 2025Article
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
- Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans.Medical & biological engineering & computing · 2025Article
- Producing micro-finite element models from real-time clinical CT scanners: calibration, validation and material mapping strategies.Frontiers in bioengineering and biotechnology · 2025Article
- From MRI to FEM: an automated pipeline for biomechanical simulations of vertebrae and intervertebral discs.Frontiers in bioengineering and biotechnology · 2024Article
- Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review.Frontiers in medicine · 2023Review
- Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.Frontiers in bioengineering and biotechnology · 2023Article
- A novel approach for tetrahedral-element-based finite element simulations of anisotropic hyperelastic intervertebral disc behavior.Frontiers in bioengineering and biotechnology · 2022Article
- Validation of a Patient-Specific Musculoskeletal Model for Lumbar Load Estimation Generated by an Automated Pipeline From Whole Body CT.Frontiers in bioengineering and biotechnology · 2022Article
- Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care.Current osteoporosis reports · 2021Review
- Finite Element Analysis of Von Mises Stress in External Fixators for Open Tibial Fractures: A Comparative Study of ASTM F1541-02 and Tibia-Based Models in Indonesian Patients.Healthcare technology lettersArticle
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
State-of-the-art preoperative biomechanical analysis for the planning of spinal surgery not only requires the generation of three-dimensional patient-specific models but also the accurate biomechanical representation of vertebral joints. The benefits offered by computational models suitable for such purposes are still outweighed by the time and effort required for their generation, thus compromising their applicability in a clinical environment. In this work, we aim to ease the integration of computerized methods into patient-specific planning of spinal surgery. We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific FE simulations (FEBio). The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical shape models onto 3D semantic segmented meshes of the vertebral models, and (c) automatic generation of patient-specific FE models of lumbar segments for the simulation of flexion-extension, lateral bending, and axial rotation movements. The automatic segmentation of FSUs was evaluated against the gold standard (manual segmentation) using 10-fold cross-validation. The obtained Dice coefficient was 93.7% on average, with a mean surface distance of 0.88 mm and a mean Hausdorff distance of 11.16 mm (
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