Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology
Abstract This paper proposes a method for the automatic measurement of proximal femoral morphological parameters based on CT images. First, construct a statistical model of the femur with generalization properties and perform Mask labeling. Second, automatically segment the femur model from CT image...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94310-9 |
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| author | Yagang Wang Qiulong Yang Jiantao Li Kaixuan Wang Miaotian Tang |
| author_facet | Yagang Wang Qiulong Yang Jiantao Li Kaixuan Wang Miaotian Tang |
| author_sort | Yagang Wang |
| collection | DOAJ |
| description | Abstract This paper proposes a method for the automatic measurement of proximal femoral morphological parameters based on CT images. First, construct a statistical model of the femur with generalization properties and perform Mask labeling. Second, automatically segment the femur model from CT images to obtain the femur sample to be tested. Then, use the GBCPD point cloud registration algorithm to complete a fast hierarchical registration between the two models, establishing point-to-point correspondences. Based on these correspondences and femoral morphological features, automatically segment the test femur sample into the shaft, neck, and head. Numerical methods are used to determine the femoral shaft axis (using PCA combined with least-squares cylinder fitting), the femoral head center and radius (least-squares sphere fitting), as well as the eccentricity and femur length. We conducted reproducibility tests of this method on 213 femurs and compared the results between automatic segmentation/measurement and manual segmentation/measurement. The Dice similarity coefficients for the femoral head, neck, and shaft reached 0.98, 0.95, and 0.99, respectively. The reproducibility errors of the anatomical standards (angles, dimensions) for automatic measurement were all lower than the errors between manual measurements, indicating that the parameter values obtained by this method exhibit good consistency with the corresponding parameter values manually identified by medical experts in the original CT images. This effectively minimizes subjective influences and can assist orthopedic surgeons in large-scale measurement analysis. |
| format | Article |
| id | doaj-art-cad29e0868004decb89ea899fa06b03c |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cad29e0868004decb89ea899fa06b03c2025-08-20T03:06:49ZengNature PortfolioScientific Reports2045-23222025-04-0115111510.1038/s41598-025-94310-9Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technologyYagang Wang0Qiulong Yang1Jiantao Li2Kaixuan Wang3Miaotian Tang4School of Computer, Xi’an University of Posts and TelecommunicationsSchool of Computer, Xi’an University of Posts and TelecommunicationsDepartment of Orthopedics, The National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, The Fourth Medical Center of Chinese PLA General HospitalDepartment of Orthopedics, The National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, The Fourth Medical Center of Chinese PLA General HospitalDepartment of Trauma and Orthopedics, Peking University People’s HospitalAbstract This paper proposes a method for the automatic measurement of proximal femoral morphological parameters based on CT images. First, construct a statistical model of the femur with generalization properties and perform Mask labeling. Second, automatically segment the femur model from CT images to obtain the femur sample to be tested. Then, use the GBCPD point cloud registration algorithm to complete a fast hierarchical registration between the two models, establishing point-to-point correspondences. Based on these correspondences and femoral morphological features, automatically segment the test femur sample into the shaft, neck, and head. Numerical methods are used to determine the femoral shaft axis (using PCA combined with least-squares cylinder fitting), the femoral head center and radius (least-squares sphere fitting), as well as the eccentricity and femur length. We conducted reproducibility tests of this method on 213 femurs and compared the results between automatic segmentation/measurement and manual segmentation/measurement. The Dice similarity coefficients for the femoral head, neck, and shaft reached 0.98, 0.95, and 0.99, respectively. The reproducibility errors of the anatomical standards (angles, dimensions) for automatic measurement were all lower than the errors between manual measurements, indicating that the parameter values obtained by this method exhibit good consistency with the corresponding parameter values manually identified by medical experts in the original CT images. This effectively minimizes subjective influences and can assist orthopedic surgeons in large-scale measurement analysis.https://doi.org/10.1038/s41598-025-94310-9CT bone imageFemur morphometric parametersPoint cloud semantic segmentationAutomatic measurement |
| spellingShingle | Yagang Wang Qiulong Yang Jiantao Li Kaixuan Wang Miaotian Tang Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology Scientific Reports CT bone image Femur morphometric parameters Point cloud semantic segmentation Automatic measurement |
| title | Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology |
| title_full | Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology |
| title_fullStr | Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology |
| title_full_unstemmed | Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology |
| title_short | Automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology |
| title_sort | automatic measurement of proximal femoral morphological parameters using point cloud semantic segmentation technology |
| topic | CT bone image Femur morphometric parameters Point cloud semantic segmentation Automatic measurement |
| url | https://doi.org/10.1038/s41598-025-94310-9 |
| work_keys_str_mv | AT yagangwang automaticmeasurementofproximalfemoralmorphologicalparametersusingpointcloudsemanticsegmentationtechnology AT qiulongyang automaticmeasurementofproximalfemoralmorphologicalparametersusingpointcloudsemanticsegmentationtechnology AT jiantaoli automaticmeasurementofproximalfemoralmorphologicalparametersusingpointcloudsemanticsegmentationtechnology AT kaixuanwang automaticmeasurementofproximalfemoralmorphologicalparametersusingpointcloudsemanticsegmentationtechnology AT miaotiantang automaticmeasurementofproximalfemoralmorphologicalparametersusingpointcloudsemanticsegmentationtechnology |