DOI QR코드

DOI QR Code

Region of Interest Localization for Bone Age Estimation Using Whole-Body Bone Scintigraphy

  • Do, Thanh-Cong (Dept. of AI Convergence, Chonnam National University) ;
  • Yang, Hyung Jeong (Dept. of AI Convergence, Chonnam National University) ;
  • Kim, Soo Hyung (Dept. of AI Convergence, Chonnam National University) ;
  • Lee, Guee Sang (Dept. of AI Convergence, Chonnam National University) ;
  • Kang, Sae Ryung (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Min, Jung Joon (Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School)
  • 투고 : 2021.04.02
  • 심사 : 2021.04.22
  • 발행 : 2021.06.30

초록

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (NRF-2020R1A2B5B01002085) and This study was financially supported by Chonnam National University (Grant number: 2018-3359)

참고문헌

  1. D.W. Belsky, A. Caspi, R. Houts, H.J. Cohen, D.L. Corcoran, A. Danese, et al., "Quantification of Biological Aging in Young Adults," Proceedings of the National Academy of Sciences, Vol. 112, No. 30, pp. E4104-E4110, The Rockefeller University, USA, Jul. 2015 https://doi.org/10.1073/pnas.1506264112
  2. N. Barzilai, et al., "The place of genetics in ageing research," Nature Reviews Genetics, Vol. 13, No. 8, pp. 589-594, Jul. 2012 https://doi.org/10.1038/nrg3290
  3. H. Heyn, N. Li, et al., "Distinct DNA methylomes of newborns and centenarians," Proc Natl Acad Sci USA, Vol. 109, No. 26, pp. 10522-10527, University of Southern California, USA, Jun. 2012 https://doi.org/10.1073/pnas.1120658109
  4. Young Gon Kang, Eunkyung Suh, Jae-woo Lee, Dong Wook Kim, Kyung Hee Cho, Chul-Young Bae, "Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study," Clinical interventions in aging, Vol. 13, pp. 429-436, Jan. 2018 https://doi.org/10.2147/CIA.S157014
  5. G.A. Borkan, A.H. Norris, "Assessment of biological age using a profile of physical parameters," Journal of Gerontology, Vol. 35, No. 2, pp.177-184, Mar. 1980 https://doi.org/10.1093/geronj/35.2.177
  6. M. Uttley, M.H. Crawford, "Efficacy of a composite biological age score to predict ten-year survival among Kansas and Nebraska Mennonites," Human Biology, Vol. 66, No. 1, pp. 121-144, Feb. 1994
  7. W.W. Greulich, S.I. Pyle, Radiographic atlas of skeletal development of hand wrist, Stanford University Press, California, 1959
  8. J.M. Tanner, MJR. Healy, H. Goldstein, N. Cameron, Assessment of skeletal maturity and prediction of adult height (TW3 method) 3rd edition, Saunders Company, 2001
  9. H.H. Thodberg, S. Kreiborg, A. Juul, K.D. Pedersen, "The BoneXpert method for automated determination of skeletal maturity," IEEE Transactions on Medical Imaging, IEEE, Vol. 28, No. 1, pp. 52-66, Jan. 2009 https://doi.org/10.1109/TMI.2008.926067
  10. J.H. Lee, Y.J. Kim, K.G. Kim, "Bone age estimation using deep learning and hand X-ray images," Biomedical Engineering Letters, Vol. 10, pp. 323-331, Mar. 2020 https://doi.org/10.1007/s13534-020-00151-y
  11. W. Brenner, N. Sieweke, K.H Bohuslavizki, W.U. Kampen, M. Zuhayra, M. Clausen, E. Henze, "Age and sex-related bone uptake of Tc-99m-HDP measured by whole-body bone scanning," Nuclear medicine, Vol. 39, No. 5, pp. 127-132, Feb. 2000
  12. V.D. Kakhki, S.R. Zakavi, "Age-related normal variants of sternal uptake on bone scintigraphy" Clinical Nuclear Medicine, Vol. 31, No. 2, pp. 63-67, Feb. 2006 https://doi.org/10.1097/01.rlu.0000195916.67925.42
  13. P.D.C. Nguyen, E.T. Baek, H.J. Yang, S.H. Kim, S.R. Kang, J.J. Min, "Multiple Inputs Deep Neural Networks for Bone Age Estimation Using Whole-Body Bone Scintigraphy," Journal of Korea Multimedia Society, Vol. 22, No. 12, pp. 1376-1384, 2019 https://doi.org/10.9717/kmms.2019.22.12.1376
  14. T.V.D. Wyngaert, K. Strobel, W.U. Kampen, T. Kuwert, W.V.D. Bruggen, H.K. Mohan, et al., "The EANM Practice Guidelines for Bone Scintigraphy," European Journal of Nuclear Medicine and Molecular Imaging, Vol. 43, No. 9, pp. 1723-1738, Jun. 2016 https://doi.org/10.1007/s00259-016-3415-4
  15. M. Escobar, C. Gonzalez, F. Torres, L. Daza, G. Triana, P. Arbelaez, "Hand pose estimation for pediatric bone age assessment," International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 531-539, Shenzhen, China, Oct. 2019
  16. Y. Wang, Q. Zhang, J. Han, Y. Jia, "Application of Deep learning in Bone age assessment," IOP Conf. Series: Earth Environmental Science, Vol. 199, No. 3, 2018
  17. M.W. Nadeem, H.G. Goh, A. Ali, M. Hussain, M.A. Khan, V.a. Ponnusamy, "Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions. Diagnostics," Diagnostics (Basel), Vol. 10, No.781, Oct. 2020
  18. C. Spampinato, S. Palazzo, D. Giordano, M. Aldinucci, and R. Leonardi, "Deep learning for automated skeletal bone age assessment in x-ray images," Medical image analysis, Vol. 36, pp. 41-51, Feb. 2017 https://doi.org/10.1016/j.media.2016.10.010
  19. V. I. Iglovikov, A. Rakhlin, A.A. Kalinin, A.A. Shvets, "Paediatric bone age assessment using deep convolutional neural networks," Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Vol. 11045, pp. 300-308, 2018
  20. H. Lee, S. Tajmir, J. Lee, M. Zissen, B.A. Yeshiwas, T.K. Alkasab, G. Choy, S. Do, "Fully Automated Deep Learning System for Bone Age Assessment," Journal of Digital Imaging, Vol. 30, No. 4, pp. 427-441, Aug. 2017 https://doi.org/10.1007/s10278-017-9955-8
  21. J. Li, W. Li, A. Gertych, B.S. Knudsen, W. Speier, C.W. Arnold, "An attention-based multi-resolution model for prostate whole slide image classification and localization," arXiv:1905.13208, May, 2019
  22. J. Fu, H. Zheng, T. Mei, "Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4438-4446, Honolulu, USA, Jul. 2017
  23. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, "Learning deep features for discriminative localization," Proceedings of the IEEE conference on computer vision and pattern recognition, Vol. 1, pp. 2921-2929, Las Vegas, USA, Jun. 2016
  24. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," Proceedings of the IEEE international conference on computer vision, pp. 618-626, Venice, Italy, Oct. 2017
  25. K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, pp. 770-778, Dec. 2016
  26. M.A. Zulkifley, S.R. Abdani, N.H. Zulkifley, "Automated Bone Age Assessment with Image Registration Using Hand X-ray Images," Appl. Sci. Vol. 10, No. 7233, Oct. 2020 https://doi.org/10.3390/app10207233