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Statistical Shape Model of the Calcaneus Bone for Comprehensive Assessment of Three-Dimensional Morphology

Calcaneus is the most susceptible tarsal bone to fractures, presenting the most challenging treatment for associated tissue damage. Intra-articular calcaneal fractures present substantial challenges for patients and surgeons due to their association with both immediate and delayed complications. The purposes of this study were to establish a method for three dimensional morphological measurements of the normal calcaneus, we develop a statistical shape model (SSM) of the calcaneus that incorporates CT scans to enable a comprehensive assessment of its three-dimensional morphology. Though surface-based registration and point-wise correspondence analysis, the left and right calcaneus bones were compared with a variety of shape analysis. The compactness and parallel analysis test on the statistical shape model yielded 7 prominent shape modes of variations (MoVs), which accounted for approximately 89% of the total 3D variations in the population of shapes. Among these modes, two captured discriminating features from both the left and right calcaneus bones (p value < 0.05). Visual inspection confirmed that these two shape modes represented abnormalities in the anterior and anteromedial parts of the calcaneus, highlighting them as the primary bony risk factors in ankle injuries. In conclusion, our study utilizing a Statistical Shape Model (SSM) has identified significant shape variations (MoVs) of the calcaneus bone which correlate significantly with the left and right sides of the body. The results of our study also demonstrate the potential utility of the SSM as a tool for providing guidance in surgical planning and treatment of calcaneus pathologies.

Calcaneus, Statistical Shape Model (SSM), Morphology

APA Style

Jie He, Zhexiao Guo, Yongjin Zhou, Xiuyun Su, Guoxian Pei. (2023). Statistical Shape Model of the Calcaneus Bone for Comprehensive Assessment of Three-Dimensional Morphology. International Journal of Biomedical Science and Engineering, 11(2), 27-32.

ACS Style

Jie He; Zhexiao Guo; Yongjin Zhou; Xiuyun Su; Guoxian Pei. Statistical Shape Model of the Calcaneus Bone for Comprehensive Assessment of Three-Dimensional Morphology. Int. J. Biomed. Sci. Eng. 2023, 11(2), 27-32. doi: 10.11648/j.ijbse.20231102.12

AMA Style

Jie He, Zhexiao Guo, Yongjin Zhou, Xiuyun Su, Guoxian Pei. Statistical Shape Model of the Calcaneus Bone for Comprehensive Assessment of Three-Dimensional Morphology. Int J Biomed Sci Eng. 2023;11(2):27-32. doi: 10.11648/j.ijbse.20231102.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. ESSEX-LOPRESTI P, PELTIER L F J C O, RESEARCH® R. The mechanism, reduction technique, and results in fractures of the os calcis [J]. 1993, 290: 3-16.
2. ALLEGRA P R, RIVERA S, DESAI S S, et al. Intra-articular calcaneus fractures: current concepts review [J]. 2020, 5 (3): 2473011420927334.
3. SPIERINGS K E, MIN M, NOOIJEN L E, et al. Managing the open calcaneal fracture: A systematic review [J]. 2019, 25 (6): 707-13.
4. VOSOUGHI A R, BORAZJANI R, GHASEMI N, et al. Different types and epidemiological patterns of calcaneal fractures based on reviewing CT images of 957 fractures [J]. 2022, 28 (1): 88-92.
5. PRANATA Y D, WANG K-C, WANG J-C, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images [J]. 2019, 171: 27-37.
6. RAHMANIAR W, WANG W-J J A S. Real-time automated segmentation and classification of calcaneal fractures in CT images [J]. 2019, 9 (15): 3011.
7. BOOZ C, NöSKE J, ALBRECHT M H, et al. Traumatic bone marrow edema of the calcaneus: evaluation of color-coded virtual non-calcium dual-energy CT in a multi-reader diagnostic accuracy study [J]. 2019, 118: 207-14.
8. SCHMUTZ B, LüTHI M, SCHMUTZ-LEONG Y K, et al. Morphological analysis of Gissane’s angle utilising a statistical shape model of the calcaneus [J]. 2021, 141: 937-45.
9. QIANG M, ZHANG K, CHEN Y, et al. Computer-assisted virtual surgical technology in pre-operative design for the reconstruction of calcaneal fracture malunion [J]. 2019, 43: 1669-77.
10. ARENA C B, SRIPANICH Y, LEAKE R, et al. Assessment of hindfoot alignment comparing weightbearing radiography to weightbearing computed tomography [J]. 2021, 42 (11): 1482-90.
11. MELINSKA A U, ROMASZKIEWICZ P, WAGEL J, et al. Statistical, morphometric, anatomical shape model (atlas) of calcaneus [J]. 2015, 10 (8): e0134603.
12. KRäHENBüHL N, LENZ A L, LISONBEE R J, et al. Morphologic analysis of the subtalar joint using statistical shape modeling [J]. 2020, 38 (12): 2625-33.
13. LENZ A L, KRäHENBüHL N, PETERSON A C, et al. Statistical shape modeling of the talocrural joint using a hybrid multi-articulation joint approach [J]. 2021, 11 (1): 7314.
14. WILLEY M C, COMPTON J T, MARSH J L, et al. Weight-bearing CT scan after tibial pilon fracture demonstrates significant early joint-space narrowing [J]. 2020, 102 (9): 796-803.
15. PAVANI C, BELVEDERE C, ORTOLANI M, et al. 3D measurement techniques for the hindfoot alignment angle from weight-bearing CT in a clinical population [J]. 2022, 12 (1): 16900.
16. RICHTER M, DUERR F, SCHILKE R, et al. Semi-automatic software-based 3D-angular measurement for Weight-Bearing CT (WBCT) in the foot provides different angles than measurement by hand [J]. 2022, 28 (7): 919-27.
17. FLISS B, LUETHI M, FUERNSTAHL P, et al. CT-based sex estimation on human femora using statistical shape modeling [J]. 2019, 169 (2): 279-86.
18. BAHL J S, ZHANG J, KILLEN B A, et al. Statistical shape modelling versus linear scaling: effects on predictions of hip joint centre location and muscle moment arms in people with hip osteoarthritis [J]. 2019, 85: 164-72.
19. KASTEN Y, DOKTOFSKY D, KOVLER I. End-to-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images; proceedings of the Machine Learning for Medical Image Reconstruction: Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 3, F, 2020 [C]. Springer.
20. FUESSINGER M A, SCHWARZ S, NEUBAUER J, et al. Virtual reconstruction of bilateral midfacial defects by using statistical shape modeling [J]. 2019, 47 (7): 1054-9.
21. STYNER M A, RAJAMANI K T, NOLTE L-P, et al. Evaluation of 3D correspondence methods for model building; proceedings of the Information Processing in Medical Imaging: 18th International Conference, IPMI 2003, Ambleside, UK, July 20-25, 2003 Proceedings 18, F, 2003 [C]. Springer.
22. AUDENAERT E A, PATTYN C, STEENACKERS G, et al. Statistical shape modeling of skeletal anatomy for sex discrimination: their training size, sexual dimorphism, and asymmetry [J]. 2019, 7: 302.