Skip to main content
SpringerLink
Log in

A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip fracture risk. In this study, we proposed an approach based on deep learning for the fast and automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments. A three-dimensional (3D) end-to-end fully convolutional neural network (CNN), which can better combine the information among neighbor slices and get more accurate segmentation results by 3D CNN, was developed for our segmentation task. The separation of cortical and trabecular bones derived from the QCT software MIAF-Femur was used as the segmentation reference. Two models with the same network structures were trained, and they achieved a dice similarity coefficient (DSC) of 97.82% and 96.53% for the periosteal and endosteal contours, respectively. Compared with MIAF-Femur, it takes half an hour to segment a case, and our CNN model takes a few minutes. To verify the excellent performance of our model for proximal femoral segmentation, we measured the volumes of different parts of the proximal femur and compared it with the ground truth, and the relative errors of femur volume between predicted result and ground truth are all less than 5%. This approach will be expected helpful to measure the bone mineral densities of cortical and trabecular bones, and to evaluate the bone strength based on FEA.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Johannesdottir F, Turmezei T, Poole KE (2014) Cortical bone assessed with clinical computed tomography at the proximal femur. J Bone Miner Res 29(4):771–783. https://doi.org/10.1002/jbmr.2199

    Article  PubMed  Google Scholar 

  2. Engelke K et al (2015) Clinical use of quantitative computed tomography (QCT) of the hip in the management of osteoporosis in adults: the 2015 ISCD official positions part I. J Clin Densitom 18(3):338–358. https://doi.org/10.1016/j.jocd.2015.06.012

    Article  PubMed  Google Scholar 

  3. Engelke K (2017) Quantitative computed tomography—current status and new developments. J Clin Densitom 20(3):309–321. https://doi.org/10.1016/j.jocd.2017.06.017

    Article  PubMed  Google Scholar 

  4. Black DM et al (2008) Proximal femoral structure and the prediction of hip fracture in men: a large prospective study using QCT. J Bone Miner Res 23(8):1326–1333. https://doi.org/10.1359/jbmr.080316

    Article  PubMed  PubMed Central  Google Scholar 

  5. Manskeet SL et al (2009) Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction. Osteoporos Int 20(3):445–453. https://doi.org/10.1007/s00198-008-0675-2

    Article  Google Scholar 

  6. Pahr DH, Zysset PK (2016) Finite element-based mechanical assessment of bone quality on the basis of in vivo images. Curr Osteoporos Rep 14(6):374–385. https://doi.org/10.1007/s11914-016-0335-y

    Article  PubMed  Google Scholar 

  7. Gong H et al (2012) Relationships between femoral strength evaluated by nonlinear finite element analysis and BMD, material distribution and geometric morphology. Ann Biomed Eng 40(7):1575–1585. https://doi.org/10.1007/s10439-012-0514-7

    Article  PubMed  Google Scholar 

  8. Bisheh H et al (2020) Hip fracture risk assessment based on different failure criteria using QCT-based finite element modeling. Comput Mater Continua 63(2):567–591

    Google Scholar 

  9. Cheng Y et al (2013) Automatic segmentation technique for acetabulum and femoral head in CT images. Pattern Recogn 46(11):2969–2984. https://doi.org/10.1016/j.patcog.2013.04.006

    Article  Google Scholar 

  10. Arezoomand S et al (2015) A 3D active model framework for segmentation of proximal femur in MR images. Int J CARS 10(1):55–66. https://doi.org/10.1007/s11548-014-1125-6

    Article  Google Scholar 

  11. Almeida DF et al (2016) Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans. Med Eng Phys 38(12):1474–1480. https://doi.org/10.1016/j.medengphy.2016.09.019

    Article  PubMed  Google Scholar 

  12. Kim J et al (2017) Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm. Comput Methods Programs Biomed 154:161–171. https://doi.org/10.1016/j.cmpb.2017.11.007

    Article  PubMed  Google Scholar 

  13. Perone CS, Cohen-Adad J (2019) Promises and limitations of deep learning for medical image segmentation. J Med Artif Intell 2:1–1. https://doi.org/10.21037/jmai.2019.01.01

    Article  Google Scholar 

  14. Hesamian MH et al (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32(4):582–596. https://doi.org/10.1007/s10278-019-00227-x

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhu L et al (2019) An automatic classification of the early osteonecrosis of femoral head with deep learning. CMIR 16(10):1323–1331. https://doi.org/10.2174/1573405615666191212104639

    Article  Google Scholar 

  16. Chen F et al (2019) Three-dimensional feature-enhanced network for automatic femur segmentation. IEEE J Biomed Health Inform 23(1):243–252. https://doi.org/10.1109/JBHI.2017.2785389

    Article  PubMed  Google Scholar 

  17. Treece GM, Gee AH (2015) Independent measurement of femoral cortical thickness and cortical bone density using clinical CT. Med Image Anal 20(1):249–264. https://doi.org/10.1016/j.media.2014.11.012

    Article  CAS  PubMed  Google Scholar 

  18. Treece GM et al (2012) Imaging the femoral cortex: thickness, density and mass from clinical CT. Med Image Anal 16(5):952–965. https://doi.org/10.1016/j.media.2012.02.008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang L et al (2020) Muscle density discriminates hip fracture better than computed tomography X-ray absorptiometry hip areal bone mineral density. J Cachexia Sarcopenia Muscle 11(6):1799–1812. https://doi.org/10.1002/jcsm.12616

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wang L et al (2018) QCT of the femur: comparison between QCTPro CTXA and MIAF Femur. Bone 120:262–270. https://doi.org/10.1016/j.bone.2018.10.016

    Article  PubMed  Google Scholar 

  21. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60. https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

  22. Milletari F et al (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision pp 565–571. https://doi.org/10.1109/3DV.2016.79

  23. Deniz CM et al (2018) Segmentation of the proximal femur from MR images using deep convolutional neural networks. Sci Rep 8:16485. https://doi.org/10.1038/s41598-018-34817-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yu D et al (2014) Mixed pooling for convolutional neural networks. In: Int conference Rough Sets Knowledge Technol. https://doi.org/10.1007/978-3-319-11740-9_34

    Article  Google Scholar 

  25. Kingma D P, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980. https://arxiv.org/abs/1412.6980

  26. Taha A, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool BMC Med Imaging. 15(29). https://doi.org/10.1186/s12880-015-0068-x

  27. Poole KE et al (2012) Cortical thickness mapping to identify focal osteoporosis in patients with hip fracture. PLoS ONE 7(2):e38466. https://doi.org/10.1371/journal.pone.0038466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ronneberger O et al (2015) U-Net: convolutional networks for biomedical image segmentation. arXiv:1505.0459. https://arxiv.org/abs/1505.04597

  29. Knowles NK (2016) Quantitative computed tomography (QCT) derived bone mineral density (BMD) in finite element studies: a review of the literature. J EXP ORTOP 3(1):36. https://doi.org/10.1186/s40634-016-0072-2

    Article  Google Scholar 

Download references

Acknowledgements

We thank Klaus Engelke of FAU, Germany, for his technical help and comments.

Funding

This work was supported in part by the Shaanxi Provincial Natural Science Foundation of China (Grant No. 2020SF-377), Xi’an Key Laboratory of Advanced Controlling and Intelligent Processing (ACIP), China (2019220714SYS022CG044). L. Wang and X. Cheng’s work was also in part supported by the National Natural Science Foundation of China (Grant Nos. 81901718, 81771831, 81971617), the Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund (Grant No. L172019), and Beijing JST Research Funding (Grant No. 8002–903-02).

Author information

Authors and Affiliations

  1. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Yu Deng & Shaojie Tang

  2. Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China

    Ling Wang & Xiaoguang Cheng

  3. College of Computing, Michigan Technological University, Houghton, MI, 49931, USA

    Chen Zhao & Weihua Zhou

  4. Xi’an Key Laboratory of Advanced Controlling and Intelligent Processing (ACIP), Xi’an, , 71021, Shaanxi, China

    Shaojie Tang

  5. Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA

    Hong-Wen Deng

Authors
  1. Yu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ling Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaojie Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoguang Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hong-Wen Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Weihua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaojie Tang.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yu Deng and Ling Wang are joint first authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, Y., Wang, L., Zhao, C. et al. A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images. Med Biol Eng Comput 60, 1417–1429 (2022). https://doi.org/10.1007/s11517-022-02529-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-022-02529-9

Keywords

Search

Navigation