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Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration

  • Dimitrios DamopoulosEmail author
  • Till Dominic Lerch
  • Florian Schmaranzer
  • Moritz Tannast
  • Christophe Chênes
  • Guoyan ZhengEmail author
  • Jérôme Schmid
Original Article
  • 129 Downloads

Abstract

Background

Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before.

Purpose

The aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery.

Methods

The proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physically based deformable model. The input to the system are the radial slices and the manually specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth.

Results

The achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55%, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head subregion, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm.

Conclusions

We validated a semiautomated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.

Keywords

Radial imaging of the hip Proximal femur 3D reconstruction Segmentation Random forest Deformable model 

Notes

Funding

This study was funded by the Swiss National Science Foundation (Grant number 205321_163224).

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individuals included in the study.

References

  1. 1.
    Chughtai M, Piuzzi NS, Khlopas A, Jones LC, Goodman SB, Mont MA (2017) An evidence-based guide to the treatment of osteonecrosis of the femoral head. Bone Jt J 99(10):1267–1279CrossRefGoogle Scholar
  2. 2.
    Sullivan JP, Griffith TB, Park CN, Ranawat AS (2017) Advances in 2D and 3D imaging for FAI surgical planning. In: Hip joint restoration. Springer, New York, pp 277–285Google Scholar
  3. 3.
    Leunig M, Beaulé PE, Ganz R (2009) The concept of femoroacetabular impingement: current status and future perspectives. Clin Orthop Relat Res 467(3):616–622CrossRefGoogle Scholar
  4. 4.
    Tannast M, Siebenrock KA, Anderson SE (2007) Femoroacetabular impingement: radiographic diagnosis—what the radiologist should know. Am J Roentgenol 188(6):1540–1552CrossRefGoogle Scholar
  5. 5.
    Steppacher SD, Huemmer C, Schwab JM, Tannast M, Siebenrock KA (2014) Surgical hip dislocation for treatment of femoroacetabular impingement: factors predicting 5-year survivorship. Clin Orthop Relat Res 472(1):337–348CrossRefGoogle Scholar
  6. 6.
    Steppacher SD, Lerch TD, Gharanizadeh K, Liechti EF, Werlen SF, Puls M, Tannast M, Siebenrock KA (2014) Size and shape of the lunate surface in different types of pincer impingement: theoretical implications for surgical therapy. Osteoarthr Cartil 22(7):951–958CrossRefGoogle Scholar
  7. 7.
    Lerch TD, Todorski IA, Steppacher SD, Schmaranzer F, Werlen SF, Siebenrock KA, Tannast M (2018) Prevalence of femoral and acetabular version abnormalities in patients with symptomatic hip disease: a controlled study of 538 hips. Am J Sports Med 46(1):122–134.  https://doi.org/10.1177/0363546517726983 CrossRefPubMedGoogle Scholar
  8. 8.
    Morita D, Hasegawa Y, Okura T, Osawa Y, Ishiguro N (2017) Long-term outcomes of transtrochanteric rotational osteotomy for non-traumatic osteonecrosis of the femoral head. Bone Jt J 99(2):175–183CrossRefGoogle Scholar
  9. 9.
    Petchprapa CN, Dunham KS, Lattanzi R, Recht MP (2013) Demystifying radial imaging of the hip. Radiographics 33(3):E97–E112CrossRefGoogle Scholar
  10. 10.
    Chana R, Noorani A, Ashwood N, Chatterji U, Healy J, Baird P (2006) The role of MRI in the diagnosis of proximal femoral fractures in the elderly. Injury 37(2):185–189CrossRefGoogle Scholar
  11. 11.
    Cabarrus MC, Ambekar A, Lu Y, Link TM (2008) MRI and CT of insufficiency fractures of the pelvis and the proximal femur. Am J Roentgenol 191(4):995–1001CrossRefGoogle Scholar
  12. 12.
    Sutter R, Dietrich TJ, Zingg PO, Pfirrmann CW (2012) How useful is the alpha angle for discriminating between symptomatic patients with cam-type femoroacetabular impingement and asymptomatic volunteers? Radiology 264(2):514–521CrossRefGoogle Scholar
  13. 13.
    Klenke FM, Hoffmann DB, Cross BJ, Siebenrock KA (2015) Validation of a standardized mapping system of the hip joint for radial MRA sequencing. Skelet Radiol 44(3):339–343CrossRefGoogle Scholar
  14. 14.
    Domayer SE, Mamisch TC, Kress I, Chan J, Kim YJ (2010) Radial dGEMRIC in developmental dysplasia of the hip and in femoroacetabular impingement: preliminary results. Osteoarthr Cartil 18(11):1421–1428CrossRefGoogle Scholar
  15. 15.
    Zilkens C, Tiderius CJ, Krauspe R, Bittersohl B (2015) Current knowledge and importance of dGEMRIC techniques in diagnosis of hip joint diseases. Skelet Radiol 44(8):1073–1083CrossRefGoogle Scholar
  16. 16.
    Riley GM, McWalter EJ, Stevens KJ, Safran MR, Lattanzi R, Gold GE (2015) MRI of the hip for the evaluation of femoroacetabular impingement; past, present, and future. J Magn Reson Imaging 41(3):558–572CrossRefGoogle Scholar
  17. 17.
    Schmaranzer F, Todorski IAS, Lerch TD, Schwab J, Cullmann-Bastian J, Tannast M (2017) Intra-articular lesions: imaging and surgical correlation. In: Seminars in musculoskeletal radiology, vol 21, No. 05. Thieme Medical Publishers, pp 487–506Google Scholar
  18. 18.
    Schmaranzer F, Haefeli PC, Hanke MS, Liechti EF, Werlen SF, Siebenrock KA, Tannast M (2017) How does the dGEMRIC index change after surgical treatment for FAI? A prospective controlled study: preliminary results. Clin Orthop Relat Res 475(4):1080–1099CrossRefGoogle Scholar
  19. 19.
    Rathnayaka K, Momot KI, Noser H, Volp A, Schuetz MA, Sahama T, Schmutz B (2012) Quantification of the accuracy of MRI generated 3D models of long bones compared to CT generated 3D models. Med Eng Phys 34(3):357–363CrossRefGoogle Scholar
  20. 20.
    Lerch T, Degonda C, Zheng G, Todorski I, Schmaranzer F, Ecker T, Siebenrock K, Tannast M (2017) MR-based 3D PAO planning and simulation of hip impingement is as accurate as CT-based 3D models. German Congress of Orthopedic and Trauma Surgery (DKOU 2017)Google Scholar
  21. 21.
    Xia Y, Fripp J, Chandra SS, Schwarz R, Engstrom C, Crozier S (2013) Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys Med Biol 58(20):7375CrossRefGoogle Scholar
  22. 22.
    Schmid J, Kim J, Magnenat-Thalmann N (2011) Robust statistical shape models for MRI bone segmentation in presence of small field of view. Med Image Anal 15(1):155–168CrossRefGoogle Scholar
  23. 23.
    Gilles B, Magnenat-Thalmann N (2010) Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med Image Anal 14(3):291–302CrossRefGoogle Scholar
  24. 24.
    Arezoomand S, Lee WS, Rakhra KS, Beaulé PE (2015) A 3D active model framework for segmentation of proximal femur in MR images. Int J Comput Assist Radiol Surg 10(1):55–66CrossRefGoogle Scholar
  25. 25.
    Chandra SS, Xia Y, Engstrom C, Crozier S, Schwarz R, Fripp J (2014) Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal 18(3):567–578CrossRefGoogle Scholar
  26. 26.
    Zeng G, Yang X, Li J, Yu L, Heng PA, Zheng G (2017) 3D U-net with multi-level deep supervision: fully automatic segmentation of proximal femur in 3D MR images. In: International workshop on machine learning in medical imaging. Springer, Cham, pp 274–282Google Scholar
  27. 27.
    Paiement A, Mirmehdi M, Xie X, Hamilton MC (2014) Integrated segmentation and interpolation of sparse data. IEEE Trans Image Process 23(1):110–125CrossRefGoogle Scholar
  28. 28.
    Van Assen HC, Danilouchkine MG, Frangi AF, Ordás S, Westenberg JJ, Reiber JH, Lelieveldt BP (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10(2):286–303CrossRefGoogle Scholar
  29. 29.
    Tu Z (2008) Auto-context and its application to high-level vision tasks. In: IEEE conference on computer vision and pattern recognition, CVPR 2008. IEEE, pp 1–8Google Scholar
  30. 30.
    Gao Y, Wang L, Shao Y, Shen D (2014) Learning distance transform for boundary detection and deformable segmentation in ct prostate images. In: International workshop on machine learning in medical imaging. Springer, Cham, pp 93–100Google Scholar
  31. 31.
    Nyúl LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19(2):143–150CrossRefGoogle Scholar
  32. 32.
    Glocker B, Zikic D, Konukoglu E, Haynor DR, Criminisi A (2013) Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 262–270Google Scholar
  33. 33.
    Criminisi A, Robertson D, Konukoglu E, Shotton J, Pathak S, White S, Siddiqui K (2013) Regression forests for efficient anatomy detection and localization in computed tomography scans. Med Image Anal 17(8):1293–1303CrossRefGoogle Scholar
  34. 34.
    Schmid J, Magnenat-Thalmann N (2008) MRI bone segmentation using deformable models and shape priors. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 119–126Google Scholar
  35. 35.
    Volino P, Magnenat-Thalmann N (2000) Implementing fast cloth simulation with collision response. In: Proceedings of the computer graphics international. IEEE, pp 257–266Google Scholar
  36. 36.
    Cootes TF, Hill A, Taylor CJ, Haslam J (1993) The use of active shape models for locating structures in medical images. In: Biennial international conference on information processing in medical imaging. Springer, Berlin, pp 33–47Google Scholar
  37. 37.
    Kraevoy V, Sheffer A (2006) Mean-value geometry encoding. Int J Shape Model 12(01):29–46CrossRefGoogle Scholar
  38. 38.
    Kumar S (2003) Discriminative random fields: a discriminative framework for contextual interaction in classification. In: Proceedings of the 9th IEEE international conference on computer vision, 2003. IEEE, pp 1150–1157Google Scholar
  39. 39.
    Chu C, Chen C, Liu L, Zheng G (2015) Facts: fully automatic ct segmentation of a hip joint. Ann Biomed Eng 43(5):1247–1259CrossRefGoogle Scholar
  40. 40.
    Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128CrossRefGoogle Scholar
  41. 41.
    Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341CrossRefGoogle Scholar
  42. 42.
    Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas OM, Das T, Jena R, Price SJ (2012) Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 369–376Google Scholar
  43. 43.
    Mahapatra D (2014) Analyzing training information from random forests for improved image segmentation. IEEE Trans Image Process 23(4):1504–1512CrossRefGoogle Scholar
  44. 44.
    Montillo A, Shotton J, Winn J, Iglesias JE, Metaxas D, Criminisi A (2011) Entangled decision forests and their application for semantic segmentation of CT images. In: Biennial international conference on information processing in medical imaging. Springer, Berlin, pp 184–196Google Scholar
  45. 45.
    Zikic D, Glocker B, Criminisi A (2014) Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med Image Anal 18(8):1262–1273CrossRefGoogle Scholar
  46. 46.
    Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N (2011) Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2):378–390CrossRefGoogle Scholar
  47. 47.
    Louppe G (2014) Understanding random forests: from theory to practice. arXiv Preprint arXiv:1407.7502
  48. 48.
    Criminisi A, Shotton J (eds) (2013) Decision forests for computer vision and medical image analysis. Springer, BerlinGoogle Scholar
  49. 49.
    Damopoulos D, Glocker B, Zheng G (2017) Automatic localization of the lumbar vertebral landmarks in CT images with context features. In: International workshop and challenge on computational methods and clinical applications in musculoskeletal imaging. Springer, Cham, pp 59–71Google Scholar
  50. 50.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  51. 51.
    Kosov S (2013) Direct graphical models C++ library. http://research.project-10.de/dgm/
  52. 52.
    Woolson RF (2007) Wilcoxon signed-rank test. Wiley, New York, pp 4739–4740.  https://doi.org/10.1002/9780471462422.eoct979 CrossRefGoogle Scholar
  53. 53.
    Li H, Johnson T (2014) Wilcoxon’s signed-rank statistic: what null hypothesis and why it matters. Pharmaceutical statistics 13(5):281–285CrossRefGoogle Scholar
  54. 54.
    Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca RatonCrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Department of Orthopaedic Surgery and Traumatology, InselspitalUniversity of BernBernSwitzerland
  3. 3.School of Health Sciences - GenevaHES-SO University of Applied Sciences and Arts Western SwitzerlandGenevaSwitzerland

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