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Kakavand R, Palizi M, Tahghighi P, Ahmadi R, Gianchandani N, Adeeb S, Souza R, Edwards WB, Komeili A. Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage. Sci Rep 2024; 14:2748. [PMID: 38302524 PMCID: PMC10834430 DOI: 10.1038/s41598-024-52548-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024] Open
Abstract
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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Affiliation(s)
- Reza Kakavand
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT 216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Mehrdad Palizi
- Civil and Environmental Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, Canada
| | - Peyman Tahghighi
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT 216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Reza Ahmadi
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT 216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Neha Gianchandani
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT 216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Samer Adeeb
- Civil and Environmental Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, Canada
| | - Roberto Souza
- Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - W Brent Edwards
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT 216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Canada
| | - Amin Komeili
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT 216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada.
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Canada.
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