Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subjec...

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Vydané v:Scientific reports Ročník 14; číslo 1; s. 2748 - 12
Hlavní autori: Kakavand, Reza, Palizi, Mehrdad, Tahghighi, Peyman, Ahmadi, Reza, Gianchandani, Neha, Adeeb, Samer, Souza, Roberto, Edwards, W. Brent, Komeili, Amin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 02.02.2024
Nature Publishing Group
Nature Portfolio
Predmet:
639/166/985
639/705/1042
Automation
Biomechanics
Cartilage (articular)
Femur
Humanities and Social Sciences
Image processing
Knee
Mathematical models
multidisciplinary
Science
Science (multidisciplinary)
Strain
Tibia
ISSN:2045-2322, 2045-2322
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Shrnutí:Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts ( https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-52548-9