Purpose
Methods
Results
Conclusions
Level of Evidence
Introduction
Methods
Study Population and MR Image Acquisition
Manual Segmentation of Proximal Femur Volume
Cam Volume, Surface Area, and Height Measurements
Statistical Analyses
Results
Proximal Femoral Bone Segmentation


Cam Morphology in PT Patients
Male (n = 9) | Female (n = 6) | |||
---|---|---|---|---|
Baseline | 12-Month | Baseline | 12-Month | |
Volume (mm3) | 1269.19 ± 301.28 | 1287.73 ± 381.24 | 544.69 ± 204.95 | 550.06 ± 128.22 |
Surface area (mm2) | 1524.56 ± 160.14 | 1490.56 ± 200.80 | 885.34 ± 161.95 | 925.22 ± 108.21 |
Maximum height (mm) | 4.36 ± 1.46 | 4.32 ± 1.42 | 3.05 ± 0.69 | 2.96 ± 0.56 |
Average height (mm) | 2.18 ± 0.69 | 2.18 ± 0.64 | 1.40 ± 0.36 | 1.43 ± 0.29 |


Cam Morphology in AS Patients
Male (n = 18) | Female (n = 10) | |||
---|---|---|---|---|
Baseline | 12-Month | Baseline | 12-Month | |
Volume (mm3) | 1342.73 ± 678.87 | 718.08 ± 552.91 | 498.93 ± 325.57 | 240.24 ± 186.58 |
Surface area (mm2) | 1520.32 ± 253.19 | 1030.62 ± 457.30 | 781.81 ± 327.82 | 482.97 ± 268.39 |
Maximum height (mm) | 4.30 ± 1.65 | 3.42 ± 1.47 | 2.85 ± 1.19 | 2.24 ± 1.11 |
Average height (mm) | 2.17 ± 0.95 | 1.52 ± 0.75 | 1.40 ± 0.66 | 0.94 ± 0.58 |

Male (n = 18) | Female (n =10) | |||
---|---|---|---|---|
Median | Interquartile Range | Median | Interquartile Range | |
Volume (mm3) | 653.5 | 399.3 to 768.1 | 151.0 | 101.4 to 387.6 |
Surface area (mm2) | 330.3 | 266.2 to 615.9 | 252.4 | 132.3 to 321.5 |
Maximum height (mm) | 0.81 | 0.38 to 1.59 | 0.58 | 0.39 to 0.99 |
Average height (mm) | 0.72 | 0.42 to 0.90 | 0.44 | 0.24 to 0.69 |

Discussion
Proximal Femur Segmentation
Cam Morphology in PT Patients
Cam Morphology in AS Patients
Limitations
Conclusions
Acknowledgments
Supplementary Material
- ICMJE author disclosure forms

References
- The Warwick Agreement on femoroacetabular impingement syndrome (FAI syndrome): An international consensus statement.Brit J Sports Med. 2016; 50: 1169-1176
- Femoroacetabular impingement: Defining the condition and its role in the pathophysiology of osteoarthritis.J Am Acad Orthop Surg. 2013; 21: S7-S15
- Femoroacetabular impingement syndrome.Current Sports Med Rep. 2020; 19: 360-366
- Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from 3D magnetic resonance images (Preprint).arXiv. 2021; 211202723
- Protocol for a multi-centre randomised controlled trial comparing arthroscopic hip surgery to physiotherapy-led care for femoroacetabular impingement (FAI): The Australian FASHIoN trial.BMC Musculoskel Disord. 2017; 18: 406
- Sex differences in patients with CAM deformities with femoroacetabular impingement: 3-Dimensional computed tomographic quantification.Arthroscopy. 2015; 31: 2301-2306
- 3D CT segmentation of CAM type femoroacetabular impingement –reliability and relationship of CAM lesion with anthropomorphic features.Br J Radiol. 2018; 91 (20180371)
- Current knowledge and importance of dGEMRIC techniques in diagnosis of hip joint diseases.Skel Radiol. 2015; 44: 1073-1083
- Measures of the amount of ecologic association between species.Ecology. 1945; 26: 297-302
- Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge.Med Image Anal. 2014; 18: 359-373
- Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool.BMC Med Imag. 2015; 15: 29
- The visualization toolkit.4th ed. Kitware, New York, NY2006
- Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using mr imaging.Physics Med Biol. 2015; 60: 7601-7616
- SciPy 1.0: Fundamental algorithms for scientific computing in Python.Nature Methods. 2020; 17: 261-272
- A lightweight rapid application development framework for biomedical image analysis.Computer Methods Prog Biomed. 2018; 164: 193-205
- Latent3DU-net: Multi-level latent shape space constrained 3D U-net for automatic segmentation of the proximal femur from radial MRI of the hip.in: Paper presented at Machine Learning in Medical Imaging. Cham, Copenhagen, Denmark2018
- Deep learning-based automatic segmentation of the proximal femur from MR images.in: Zheng G. Tian W. Zhuang X. Intelligent orthopaedics: Artificial intelligence and smart image-guided technology for orthopaedics. Springer Singapore, Singapore2018: 73-79
- Semantic segmentation of femur bone from MRI images of patients with hematologic malignancies.in: Paper presented at 2020 IEEE Region 10 Conference (Tencon), Osaka, Japan; Nov 16-19. 2020
- Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols.Comput Med Imag Graphics. 2020; 81: 101715
- Segmentation of the proximal femur from MR images using deep convolutional neural networks.Scientif Rep. 2018; 8: 16485
- MRI-and CT-based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome.J Orthop Res. 2022; 40: 1174-1181
- Comparison of 3D bone models of the knee joint derived from CT and 3T MR imaging.Eur J Radiol. 2017; 93: 178-184
- Multi-centre randomised controlled trial comparing arthroscopic hip surgery to physiotherapist-led care for femoroacetabular impingement (FAI) syndrome on hip cartilage metabolism: The Australian FASHIoN trial.BMC Musculoskel Disord. 2021; 22: 697
- Prospective in vivo comparison of damaged and healthy-appearing articular cartilage specimens in patients with femoroacetabular impingement: Comparison of T2 mapping, histologic endpoints, and arthroscopic grading.Arthroscopy. 2016; 32: 1601-1611
- Delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) in femoacetabular impingement.J Orthop Res. 2011; 29: 1305-1311
- Cartilage T1ρ and T2 relaxation times in patients with mild-to-moderate radiographic hip osteoarthritis.Arthritis Rheumatol. 2015; 67: 1548-1556
- Toward patient-specific articular contact mechanics.J Biomech. 2015; 48: 779-786
- Discrete element and finite element methods provide similar estimations for hip joint contact mechanics during walking gait.J Biomech. 2021; 115: 110163
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Footnotes
The authors report the following potential conflicts of interest or sources of funding: S.S.C. reports grants from the National Health and Medical Research Council, during the conduct of this study. S.C. reports grants from National Health and Medical Research Council, during the conduct of the study; and is the director of Magnetica Pty Ltd, outside the submitted work. D.J.H. reports personal fees from Pfizer, Lilly, TLCBio, Novartis, Tissuegene, and Biobone, outside the submitted work. J.F. reports grants from National Health and Medical Research Council, during the conduct of the study. C.E. reports grants from National Health and Medical Research Council, during the conduct of the study.
Full ICMJE author disclosure forms are available for this article online, as Supplemental Material.
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