Injuries involving the Atlas (C1) and Axis (C2) vertebrae of the cervical spine present significant clinical challenges due to their complex anatomy and potential for severe neurological impairment. Traditional imaging methods often lack the detailed visualization required for precise surgical planning. This study aimed to develop high-resolution 3D models of the C1 and C2 vertebrae to perform a comprehensive morphometric analysis, identify gender differences, and assess bilateral symmetry to enhance surgical accuracy. A retrospective analysis was conducted using CT scans from 500 patients aged 18 and older from a single-center hospital. Three-dimensional models were generated using InVesalius 3.1 and visualized with Meshmixer. Morphometric measurements included screw placement angles, lamina length and height, bicortical diameters, and pedicle widths. Statistical analyses were conducted using SPSS, with the Student's -test applied for gender and bilateral comparisons. Significant gender differences were found in certain measurements, such as pedicle width (4.85 ± 0.90 mm in males vs. 4.60 ± 0.85 mm in females, = 0.048) and C2 lamina height (12.90 ± 1.40 mm in males vs. 12.40 ± 1.25 mm in females, = 0.033). Most measurements exhibited bilateral symmetry, supporting their applicability across genders. These results align with previous studies and highlight the importance of tailored surgical approaches. Three-dimensional models of the C1 and C2 provide comprehensive morphometric data that enhance preoperative planning and surgical precision. Integrating these models into clinical practice can reduce intraoperative risks and improve patient outcomes in cervical spine surgeries.
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http://dx.doi.org/10.3390/jcm14010243 | DOI Listing |
J Clin Med
January 2025
Department of Orthopaedics, Phramongkutklao Hospital and College of Medicine, Bangkok 10400, Thailand.
Injuries involving the Atlas (C1) and Axis (C2) vertebrae of the cervical spine present significant clinical challenges due to their complex anatomy and potential for severe neurological impairment. Traditional imaging methods often lack the detailed visualization required for precise surgical planning. This study aimed to develop high-resolution 3D models of the C1 and C2 vertebrae to perform a comprehensive morphometric analysis, identify gender differences, and assess bilateral symmetry to enhance surgical accuracy.
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Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study simplifies the organ areas to polygons and focuses solely on the endpoint positioning.
View Article and Find Full Text PDFInt J Mol Sci
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Department of Biology, School of Medicine, State University of Rio de Janeiro, Professor Manuel de Abreu, 444, Avenue, Rio de Janeiro 20550-170, Brazil.
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View Article and Find Full Text PDFAnimals (Basel)
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Instituto de Investigaciones Sobre los Recursos Naturales, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58330, Michoacán, Mexico.
Body size is one of the most important traits in the life history of vertebrates. In this work, we analyzed the morphometric traits of breeding males of the eastern Pacific green sea turtle population known as the black sea turtle on the coast of Michoacan, Mexico. The morphometric analysis indicates that males have the smallest body size compared to other males in other Chelonia populations.
View Article and Find Full Text PDFJpn J Radiol
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Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
Purpose: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers.
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