Advertisement

Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

  • Rafeek Thaha
  • Sandeep P. Jogi
  • Sriram Rajan
  • Vidur Mahajan
  • Vasantha K. Venugopal
  • Amit Mehndiratta
  • Anup SinghEmail author
Original Article

Abstract

Purpose

The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibiofemoral cartilage in MR images of OA patient with and without subchondral bone abnormalities (SBA).

Methods

In this study, knee MRI data [fat-suppressed proton density-weighted, multi-echo T2-weighted (CartiGram) images] of 29 OA patients, acquired at 3.0T MR scanner, were retrospectively collected. Out of 29 data, 9 had SBA in femur bone. Initially, a semi-automatic femur cartilage segmentation based on radial intensity search approach by Akhtar et al. was implemented in-house. This algorithm was considered as the radial-search method for further comparison. In this current study, the reported radial-search (RS)-based semi-automatic cartilage segmentation method was modified using thresholding, connected component labelling, convex-hull operation and spline-based curve fitting for the improved segmentation of tibiofemoral cartilage. Cartilage was manually segmented by two experienced radiologists, and inter-reader variability was estimated using coefficient of variation (CV). The segmentation results were validated using dice coefficient (DC), Jaccard coefficient (JC) and sensitivity index measurements.

Results

DC values for segmented femur cartilage in patients with SBA were 64.6 ± 7.8% and 81.4 ± 2.8% using reported RS method and modified radial-search method, respectively. DC values for segmented femur cartilage in patients without SBA were 82.5 ± 4.5% and 84.8 ± 2.0% using RS method and modified radial method, respectively. Similarly, DC values for tibial cartilage in all OA patients were 80.4 ± 1.6% and 81.9 ± 2.4% using RS method and modified radial method, respectively. Similar segmentation results were also obtained from the T2-weighted images. Inter-reader variability result based on CV in femur cartilage was 3.40 ± 2.12% (without SBA) and 4.18 ± 3.18% (with SBA).

Conclusion

In the current study, a semi-automated segmentation of tibiofemoral cartilage was presented. Modified radial-search approach can successfully segment tibiofemoral cartilage, and the results were tested and validated on knee MRI data of OA patients with and without SBA.

Keywords

Articular cartilage Osteoarthritis MRI Segmentation Subchondral bone abnormality 

Notes

Acknowledgements

Authors acknowledge the MRI data acquisition support from Mahajan Imaging centre, New Delhi, India. The authors would like to thank Dr. Harsh Mahajan for providing the clinical insights, Ms. Madhuri Bansal, an application analyst in Mahajan Imaging Centre for her support in data collection.

Author contributions

RT, AS and AM conceived and designed the experiments. RT, SPJ, SR and AS performed the experiments. RT, AS and SPJ analysed the data. RT, SPJ, SR, VKV, VM, AS and AM contributed materials/analysis tools. RT, SPJ, SR, VKV, VM, AS and AM contributed to the writing of the manuscript.

Funding

This study was supported by the Industrial Research & Development Unit, Indian Institute of Technology Delhi (FIRP Project Number-MI01422). The funding body had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Chen D, Shen J, Zhao W, Wang T, Han L, Hamilton JL, Im H (2017) Osteoarthritis: toward a comprehensive understanding of pathological mechanism. Bone Res 5:16044–16057CrossRefGoogle Scholar
  2. 2.
    Gold GE, Chen CA, Koo S, Hargreaves BA, Bangerter NK (2009) Recent advances in MRI of articular cartilage. AJR Am J Roentgenol 193(3):628–638CrossRefGoogle Scholar
  3. 3.
    Peuna A, Hekkala J, Haapea M, Podlipská J, Guermazi A, Saarakkala S, Nieminen MT, Lammentausta E (2018) Variable angle gray level co-occurrence matrix analysis of T 2 relaxation time maps reveals degenerative changes of cartilage in knee osteoarthritis: oulu knee osteoarthritis study. J Magn Reson Imaging 47(5):1316–1327CrossRefGoogle Scholar
  4. 4.
    Akhtar S, Poh CL, Kitney RI (2007) An MRI derived articular cartilage visualization framework. Osteoarthr Cartil 15:1070–1085CrossRefGoogle Scholar
  5. 5.
    Liukkonen MK, Mononen ME, Tanska P, Saarakkala S, Nieminen T, Korhonen RK (2017) Engineering application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint. Comput Methods Biomech Biomed Eng 5842:1–11CrossRefGoogle Scholar
  6. 6.
    Poh C-L, Kitney RI (2006) Viewing interfaces for segmentation and measurement results. In: IEEE engineering in medicine and biology 27th annual conference, vol 5, pp 5132–5135Google Scholar
  7. 7.
    Hunter DJ, Zhang Y, Niu J, Goggins J, Amin S, LaValley MP, Guermazi A, Genant H, Gale D, Felson DT (2006) Increase in bone marrow lesions associated with cartilage loss: a longitudinal magnetic resonance imaging study of knee osteoarthritis. Arthritis Rheum 54:1529–1535CrossRefGoogle Scholar
  8. 8.
    Kijowski R, Blankenbaker DG, Baer GS, Graf BK (2013) Evaluation of the articular cartilage of the knee joint: value of adding a T2 mapping sequence to a routine MR imaging protocol 1. Radiology 267:503–513CrossRefGoogle Scholar
  9. 9.
    Liu F, Zhou Z, Jang H, Samsonov A, Zhao G, Kijowski R (2018) Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med 79(4):2379–2391CrossRefGoogle Scholar
  10. 10.
    Cohen ZA, McCarthy DM, Kwak SD, Legrand P, Fogarasi F, Ciaccio EJ, Ateshian GA (1999) Knee cartilage topography, thickness, and contact areas from MRI: in vitro calibration and in vivo measurements. Osteoarthr Cartil 7(1):95–109CrossRefGoogle Scholar
  11. 11.
    Koff MF, Amrami KK, Kaufman KR (2007) Clinical evaluation of T2 values of patellar cartilage in patients with osteoarthritis. Osteoarthr Cartil 15:198–204CrossRefGoogle Scholar
  12. 12.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302CrossRefGoogle Scholar
  13. 13.
    Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol XI:37–50CrossRefGoogle Scholar
  14. 14.
    Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, Hirsch BE, Woodburn J (2006) A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph 30:75–87CrossRefGoogle Scholar
  15. 15.
    Mosher TJ, Dardzinski BJ (2004) Cartilage MRI T2 relaxation time mapping: overview and applications. Semin Musculoskelet Radiol 1:355–368CrossRefGoogle Scholar
  16. 16.
    David-vaudey E, Ghosh S, Ries M, Majumdar S (2004) T2 relaxation time measurements in osteoarthritis. Magn Reson Imaging 22:673–682CrossRefGoogle Scholar
  17. 17.
    Bae KT, Shim H, Tao C, Chang S, Wang JH, Boudreau R, Kwoh CK (2009) Intra-and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method. Osteoarthr Cartil 17:1589–1597CrossRefGoogle Scholar
  18. 18.
    Jogi SP, Rafeek T, Rajan S, Rangarajan K, Singh A, Mehndiratta A (2017) Automated seed points selection based radial-search segmentation method for sagittal and coronal view knee MRI imaging. In: 26th annual meeting ISMRM-ESMRMB. vol 2, pp 2017–2019Google Scholar
  19. 19.
    Kornaat PR, Sharma R, Botha-scheepers SA, Bloem JL (2007) Bone marrow edema-like lesions change in volume in the majority of patients with osteoarthritis; associations with clinical features. Eur Radiol 17:3073–3078CrossRefGoogle Scholar
  20. 20.
    Stehling C, Baum T, Mueller-hoecker C, Liebl H, Carballido-Gamio J, Joseph GB, Majumdar S, Link TM (2011) A novel fast knee cartilage segmentation technique for T2 measurements at MR imaging data from the osteoarthritis initiative. Osteoarthr Cartil 19:984–989CrossRefGoogle Scholar
  21. 21.
    Folkesson J, Dam EB, Olsen OF, Pettersen PC (2007) Segmenting articular cartilage automatically using a voxel classification approach. IEEE Trans Med Imaging 26:106–115CrossRefGoogle Scholar
  22. 22.
    Pang J, Li P, Qiu M, Chen W, Qiao L (2015) Automatic articular cartilage segmentation based on pattern recognition from knee MRI images. J Digit Imaging 28:695–703CrossRefGoogle Scholar
  23. 23.
    Kaneko Y, Nozaki T, Yu H, Chang A, Kaneshiro K, Schwarzkopf R, Hara T, Yoshioka H (2015) Normal T2 map profile of the entire femoral cartilage using an angle/layer dependent approach. J Magn Reson Imaging 42(6):1507–1516CrossRefGoogle Scholar
  24. 24.
    Yin Y, Zhang X, Williams R, Wu X, Anderson DD, Sonka M (2010) LOGISMOS—layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint. IEEE Trans Med Imaging 29(12):2023–2037CrossRefGoogle Scholar
  25. 25.
    Ahn C, Bui TD, Lee Y, Shin J, Park H (2016) Fully automated, level set based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative. Biomed Eng Online 15(1):99CrossRefGoogle Scholar
  26. 26.
    Fripp J, Crozier S, Warfield SK, Member S, Ourselin S (2010) Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans Med Imaging 29:55–64CrossRefGoogle Scholar

Copyright information

© CARS 2020

Authors and Affiliations

  • Rafeek Thaha
    • 1
  • Sandeep P. Jogi
    • 1
    • 2
  • Sriram Rajan
    • 3
  • Vidur Mahajan
    • 3
  • Vasantha K. Venugopal
    • 3
  • Amit Mehndiratta
    • 1
    • 4
  • Anup Singh
    • 1
    • 4
    Email author
  1. 1.Centre for Biomedical EngineeringIndian Institute of Technology DelhiNew DelhiIndia
  2. 2.Department of Biomedical EngineeringASET, Amity University HaryanaGurgaonIndia
  3. 3.Mahajan Imaging CentreNew DelhiIndia
  4. 4.Department of Biomedical EngineeringAll India Institute of Medical SciencesNew DelhiIndia

Personalised recommendations