Skip to main content
Springer Nature Link
Log in

Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network

  • Published:
Save article
View saved research
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Although radiographic assessment of joint damage is essential in characterizing disease progression and prognosis in patients with rheumatoid arthritis (RA), it is often difficult even for trained radiologists to find radiographic changes on hand and foot radiographs because lesion changes are often subtle. This paper proposes a novel quantitative method for automatically detecting bone erosion on hand radiographs to assist radiologists. First, the proposed method performs with the crude segmentation of phalanges regions from hand radiograph and extracts the detailed phalanges regions by the multiscale gradient vector flow (MSGVF) Snakes method. Subsequently, the region of interest (ROI; 40 × 40 pixels) is automatically set on the contour line of the segmented phalanges by the MSVGF algorithm. Finally, these selected ROIs are identified by the presence or absence of bone erosion using a deep convolutional neural network classifier. This proposed method is applied to the hand radiographs of 30 cases with RA. The true-positive rate and the false-positive rate of the proposed method are 80.5% and 0.84%, respectively. The number of false-positive ROIs is 3.3 per case. We believe that the proposed method is useful for supporting radiologists in imaging diagnosis of RA.

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

Access this article

Log in via an institution

Subscribe and save

Springer+
from €37.37 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (Netherlands)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
The alternative text for this image may have been generated using AI.
Fig. 2
The alternative text for this image may have been generated using AI.
Fig. 3
The alternative text for this image may have been generated using AI.
Fig. 4
The alternative text for this image may have been generated using AI.
Fig. 5
The alternative text for this image may have been generated using AI.
Fig. 6
The alternative text for this image may have been generated using AI.
Fig. 7
The alternative text for this image may have been generated using AI.
Fig. 8
The alternative text for this image may have been generated using AI.
Fig. 9
The alternative text for this image may have been generated using AI.
Fig. 10
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216

    Article  Google Scholar 

  2. Aoki T, Fujii M, Yamashita Y, Takahashi H, Oki H, Hayashida Y, Saito K, Tanaka Y, Korogi Y (2014) Tomosynthesis of the wrist and hand in patients with rheumatoid arthritis, comparison with radiography and MRI. Am J Roentgenol 202(2):386–390

    Article  Google Scholar 

  3. Caffe http://caffe.berkeleyvision.org/

  4. Dou Q, Chen H, Yu L, Zhao L, Qin J, Wang D, Mok VCT, Shi L, Heng PA (2016) Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging 35(5):1182–1195

    Article  Google Scholar 

  5. Eshghi MD, Roth HR, Oda M, Chung MS, Mori K (2017) Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project., arXiv preprint arXiv:1703.04967

  6. Fan PT, Leong KH (2007) The use of biological agents in the treatment of rheumatoid. Ann Acad Med Singap 36(2):128–134

    Google Scholar 

  7. He L, Xu X, Lu H, Yang Y, Shen F, Shen HT (2017) Unsupervised cross-modal retrieval through adversarial learning., Multimedia and Expo (ICME), 2017 I.E. International Conference on. IEEE, pp. 1153–1158

  8. Ichikawa S, Kamishima T, Sutherland K, Okubo T, Katayama K (2016) Performance of computer-based analysis using temporal subtraction to assess joint space narrowing progression in rheumatoid patients. Rheum Int 36(1):101–108

    Article  Google Scholar 

  9. IMAGENET large scale visual recognition challenge 2012 (ILSVRC2012). http://imagenet.org/challenges/LSVRC/2012/results.html

  10. Jiang J, Trundle P, Ren J (2010) Medical image analysis with artificial neural networks. Comput Med Imaging Graph 34(8):617–631

    Article  Google Scholar 

  11. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, pp:1097–1105

  12. Langs G, Peloschek P, Bischof H, Kainberger F (2009) Automatic quantification of joint space narrowing and erosions in rheumatoid arthritis. IEEE Trans Med Imaging 28(1):151–164

    Article  Google Scholar 

  13. Larsen A, Dale K, Eek M (1977) Radiographic evaluation of rheumatoid arthritis and related conditions by standard reference films. Acta Radiol Diagn (Stockh) 18(4):481–491

    Article  Google Scholar 

  14. Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation: Practice and Experience 29(6). https://doi.org/10.1002/cpe.3927

  15. Lu H, Li Y, Uemura T, Ge Z, Xu X, Serikawa S, Kim H (2017) FDCNet: filtering deep convolutional network for marine organism classification. Multimedia tools and applications, 1–14

  16. Lu H, Li Y, Chen M, Kim H, Serikawa S (2017) Brain Intelligence: Go beyond artificial intelligence., Mobile Networks and Application, pp. 1–10

  17. Miao S, Wang ZJ, Liao R (2016) A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging 35(5):1352–1363

    Article  Google Scholar 

  18. Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Išgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5):1252–1261

    Article  Google Scholar 

  19. Murakami S, Kim H, Tan JK, Ishikawa S, Aoki T (2013) Development of a quantitative method for detection of periarticular osteoporosis using density features on ROIs from computed radiography images of the hand. Technological Advancements in Biomedicine for Healthcare Applications, Medical Information Science Reference 7:55–67

    Article  Google Scholar 

  20. Ngo TA, Lu Z, Carneiro G (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal 35:159–171

    Article  Google Scholar 

  21. Pan SJ, Qiang Y (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  22. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251

    Article  Google Scholar 

  23. Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, Kim L, Summers RM (2016) Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 35(5):1170–1181

    Article  Google Scholar 

  24. Sharp JT, Bluhm GB, Brook A, Brower AC, Corbett M, Decker JL, Genant HK, Gofton JP, Goodman N, Larsen A, Lidsky MD, Pussila P, Weinstein AS, Weissman BN, Young DY (1985) Reproducibility of multiple-observer scoring of radiologic abnormalities in the hands and wrists of patients with rheumatoid arthritis. Arthritis Rheum 28(1):16–24

    Article  Google Scholar 

  25. Tang J, Acton ST (2004) Vessel boundary tracking for intravital microscopy via multiscale gradient vector flow snakes. IEEE Trans Biomed Eng 51(2):316–324

    Article  Google Scholar 

  26. Unser M, Aldroubi A, Eden M (1993) B-spline signal processing: part I-theory. IEEE Trans Signal Process 41(2):821–832

    Article  MATH  Google Scholar 

  27. van der Heijde D, Landewé R, Klareskog L, Rodríguez-Valverde V, Settas L, Pedersen R, Fatenejad S (2005) Presentation and analysis of data on radiographic outcome in clinical trials: experience from the TEMPO study. Arthritis Rheum 52(1):49–60

    Article  Google Scholar 

  28. Yoshino Y, Miyajima T, Lu H, Tan J, Kim H, Murakami S, Aoki T, Tachibana R, Hirano Y, Kido S (2017) Automatic classification of lung nodules on MDCT images with the temporal subtraction technique. Int J Comput Assist Radiol Surg 12(10):1789–1798

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 16 K14279, 17 K10420.

Author information

Authors and Affiliations

  1. Kyushu Institute of Technology, 1-1, Sensui, Tobata, Kitakyushu, 804-8550, Japan

    Seiichi Murakami, Kazuhiro Hatano, JooKooi Tan & Hyoungseop Kim

  2. University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi, Kitakyusyu, 807-8555, Japan

    Seiichi Murakami & Takatoshi Aoki

Authors
  1. Seiichi Murakami
    View author publications

    Search author on:PubMed Google Scholar

  2. Kazuhiro Hatano
    View author publications

    Search author on:PubMed Google Scholar

  3. JooKooi Tan
    View author publications

    Search author on:PubMed Google Scholar

  4. Hyoungseop Kim
    View author publications

    Search author on:PubMed Google Scholar

  5. Takatoshi Aoki
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Seiichi Murakami.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Murakami, S., Hatano, K., Tan, J. et al. Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network. Multimed Tools Appl 77, 10921–10937 (2018). https://doi.org/10.1007/s11042-017-5449-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s11042-017-5449-4

Keywords