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Multiple Inputs Deep Neural Networks for Bone Age Estimation Using Whole-Body Bone Scintigraphy

  • Nguyen, Phap Do Cong (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Baek, Eu-Tteum (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Yang, Hyung-Jeong (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Kim, Soo-Hyung (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Kang, Sae-Ryung (Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital) ;
  • Min, Jung-Joon (Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School)
  • Received : 2019.11.01
  • Accepted : 2019.12.04
  • Published : 2019.12.31
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Abstract

The cosmetic and behavioral aspects of aging have become increasingly evident over the years. Physical aging in people can easily be observed on their face, posture, voice, and gait. In contrast, bone aging only becomes apparent once significant bone degeneration manifests through degenerative bone diseases. Therefore, a more accurate and timely assessment of bone aging is needed so that the determinants and its mechanisms can be more effectively identified and ultimately optimized. This study proposed a deep learning approach to assess the bone age of an adult using whole-body bone scintigraphy. The proposed approach uses multiple inputs deep neural network architectures using a loss function, called mean-variance loss. The data set was collected from Chonnam National University Hwasun Hospital. The experiment results show the effectiveness of the proposed method with a mean absolute error of 3.40 years.

Keywords

References

  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, 2015. https://doi.org/10.1073/pnas.1501574112
  2. J.H. Cole, K. Franke, and N. Cherbuin, Biomarkers of Human Aging, Springer Nature Switzerland AG, Cham, Switzerland, 2019.
  3. H.M. Aycheh, J. Seong, J. Shin, D.L. Na, B. Kang, S.W. Seo, et al., "Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study," Frontiers in Aging Neuroscience, Vol. 10, No. 252, pp.1-14, 2018. https://doi.org/10.3389/fnagi.2018.00001
  4. F. Liem, G. Varoquaux, J. Kynast, F. Beyer, S.K. Masouleh, J.M. Huntenburg, et al., "Predicting Brain-age From Multimodal Imaging Data Captures Cognitive Impairment," NeuroImage, Vol. 148, pp. 179-188, 2017. https://doi.org/10.1016/j.neuroimage.2016.11.005
  5. O. Demontiero, C. Vidal, and G. Duque, “Aging and Bone Loss: New Insights for the Clinician,” Therapeutic Advances in Musculoskeletal Disease, Vol. 4, No. 2, pp. 61-76, 2011. https://doi.org/10.1177/1759720X11430858
  6. G.M. Kiebzak, “Age-related Bone Changes,” Experimental Gerontology, Vol. 26, No. 2-3, pp. 171-187, 1991. https://doi.org/10.1016/0531-5565(91)90010-J
  7. D.D. Bikle, A. Stesin, B. Halloran, L. Steinbach, and R. Recker, “Alcohol‐Induced Bone Disease: Relationship to Age and Parathyroid Hormone Levels,” Alcoholism: Clinical and Experimental Research, Vol. 17, No. 3, pp. 690-695, 1993. https://doi.org/10.1111/j.1530-0277.1993.tb00821.x
  8. Y. Kigami, I. Yamamoto, H. Ohnishi, H. Miura, Y. Ohnaka, T. Ota, et al., “Age-related Change of Technetium-99m-HMDP Distribution in the Skeleton,” Journal of Nuclear Medicine, Vol. 37, No. 5, pp. 815-818, 1996.
  9. W. Brenner, N. Sieweke, W.U. Kampen, M. Zuhayra, E. Henze, K.H. Bohuslavizki, et al., “Age- and Sex-related Bone Uptake of Tc-99m-HDP Measured by Whole-body Bone Scanning,” Nuklearmedizin, Vol. 39, No. 5, pp. 127-132, 2000. https://doi.org/10.1055/s-0038-1632258
  10. G.M. Syed, H.W. Fielding, and B.D. Collier, “Sternal Uptake on Bone Scintigraphy: Agerelated Variants,” Nuclear Medicine Communications, Vol. 26, No. 3, pp. 253-257, 2005. https://doi.org/10.1097/00006231-200503000-00010
  11. V.D. Kakhki and S.R. Zakavi, “Age-Related Normal Variants of Sternal Uptake on Bone Scintigraphy,” Clinical Nuclear Medicine, Vol. 31, No. 2, pp. 63-67, 2006. https://doi.org/10.1097/01.rlu.0000195916.67925.42
  12. 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, 2016. https://doi.org/10.1007/s00259-016-3415-4
  13. W.W. Greulich and S.I. Pyle, Radiographic Atlas of Skeletal Development of the Hand and Wrist, Stanford University Press, Stanford, CA, 1959.
  14. D.H. Ubelaker, “Estimating Age at Death from Immature Human Skeletons: An Overview,” Journal of Forensic Sciences, Vol. 32, No. 5, pp. 1254-1263, 1987. https://doi.org/10.1520/JFS11176J
  15. N. Lynnerup, E. Belard, K.B. Olsen, B. Sejrsen, and K.D. Pedersen, “Intra-and Interobserver Error of the Greulich–Pyle Method as used on a Danish Forensic Sample,” Forensic Science International, Vol. 179, No. 24, pp. 241-246, 2008.
  16. H.H. Mince, E.F. Harris, and H.E. Berryman, “The A.B.F.O. Study of Third Molar Development and Its Use as an Estimator of Chronological Age,” Journal of Forensic Sciences, Vol. 38, No. 2, pp. 379-390, 1993.
  17. J.M. Tanne, R.H. Whitehouse, W.A. Marshall, and B.S. Carter, “Prediction of Adult Height from Height, Bone Age, and Occurrence of Menarche at Ages 4-16 with Allowance for Midparent Height,” Archives of Disease in Childhood, Vol. 50, No. 1, pp. 14-26, 1975. https://doi.org/10.1136/adc.50.1.14
  18. L. Scheuer and S. Black, Developmental Juvenile Osteology, Academic Press, London, 2000.
  19. L.A. Madeline and A.D. Elster, “Suture Closure in the Human Chondrocranium: CT Assessment,” Radiology, Vol. 196, No. 3, pp. 747-756, 1995. https://doi.org/10.1148/radiology.196.3.7644639
  20. R. Schulz, M. Muhler, S. Mutze, S. Schmidt, W. Reisinger, and A. Schmeling, “Studies on the Time Frame for Ossification of the Medial Epiphysis of the Clavicle as Revealed by CT Scans,” International Journal of Legal Medicine, Vol. 119, No. 3, pp. 142-145, 2005. https://doi.org/10.1007/s00414-005-0529-9
  21. S. Schmidt, M. Muhler, A. Schmeling, W. Reisinger, and R. Schulz, “Magnetic Resonance Imaging of the Clavicular Ossification,” International Journal of Legal Medicine, Vol. 121, No. 4, pp. 321-324, 2007. https://doi.org/10.1007/s00414-007-0160-z
  22. J.A. Garriga and S.C. Zapico, “Age Assessment in Forensic Cases: Anthropological, Odontological and Biochemical Methods for Age Estimation in the Dead,” Mathews Journal of Forensic Research, Vol. 1, No. 1, pp. 1-6, 2018.
  23. S. Brooks, “Age at Death: Reliability of Cranial and Pubic Age Indicators,” American Journal of Physical Anthropology, Vol. 13, No. 4, pp. 567-597, 1955. https://doi.org/10.1002/ajpa.1330130403
  24. T.W. Todd, “Age Changes in the Pubic Bone IV: The Interpretation of Variations in Symphyseal Area,” American Journal of Physical Anthropology, Vol. 4, No. 4, pp. 407- 424, 1921. https://doi.org/10.1002/ajpa.1330040403
  25. C.O. Lovejoy, R.S. Meindl, T.R. Pryzbeck, and R.P. Mensforth, “Chronological Metamorphosis of the Auricular Surface of the Ilium: A New Method for the Determination of Adult Skeletal Age at Death,” American Journal of Physical Anthropology, Vol. 68, No. 1, pp. 15-28, 1985. https://doi.org/10.1002/ajpa.1330680103
  26. C.R. Maillart, N. Jousset, B. Vielle, A. Gaudin, and N. Telmon, “Contribution of the Study of Acetabulum for the Estimation of Adult Subjects,” Forensic Science International, Vol. 171, No. 2-3, pp. 103-110, 2007. https://doi.org/10.1016/j.forsciint.2006.10.007
  27. N. Lottering, D.M. MacGregor, M. Meredith, and L.S. Gregory, "Error and Uncertainty of Adult Age Estimation of the Pubic Symphysis in an Australian Sub-population Using Computed Tomography," Proceedings of the American Academy of Forensic Sciences, pp. 435, 2013.
  28. M.S. Millán, C. Rissech, and D. Turbón, “New Approach to Age Estimation of Male and Female Adult Skeletons based on the Morphological Characteristics of the Acetabulum,” International Journal of Legal Medicine, Vol. 131, No. 2, pp. 501-525, 2017. https://doi.org/10.1007/s00414-016-1406-4
  29. B. Bekaert, A. Kamalandua, S.C. Zapico, W. V.D. Voorde, and R. Decorte, “Improved Age Determination of Blood and Teeth Samples Using a Selected Set of DNA Methylation Markers,” Epigenetics, Vol. 10, No. 10, pp. 1-9, 2015. https://doi.org/10.1080/15592294.2014.995536
  30. C.B. Harley, A.B. Futcher, and C.W. Greider, “Telomeres Shorten During Ageing of Human Fibroblasts,” Nature, Vol. 345, No. 6274, pp. 458-460, 1990. https://doi.org/10.1038/345458a0
  31. A. Melk, V. Ramassar, L.M.H. Helms, R. Moore, D. Rayner, K. Solez, and P.F. Halloran, “Telomere Shortening in Kidneys with Age,” Journal of the American Society of Nephrology, Vol. 11, No. 3, pp. 444-453, 2000. https://doi.org/10.1681/ASN.V113444
  32. F. Ren, C. Li, H. Xi, Y. Wen, and K. Huang, “Estimation of Human Age According to Telomere Shortening in Peripheral Blood Leukocytes of Tibetan,” The American Journal of Forensic Medicine and Pathology, Vol. 30, No. 3, pp. 252-255, 2009. https://doi.org/10.1097/PAF.0b013e318187df8e
  33. T. Takasaki, A. Tsuji, and N. Ikeda N, “Age Estimation in Dental Pulp DNA based on Human Telomere Shortening,” International Journal of Legal Medicine, Vol. 117, No. 4, pp. 232-234, 2003. https://doi.org/10.1007/s00414-003-0376-5
  34. A. Tsuji, A. Ishiko, T. Takasaki, and N. Ikeda, “Estimating Age of Humans based on Telomere Shortening,” Forensic Science International, Vol. 126, No. 3, pp. 197-199, 2002. https://doi.org/10.1016/S0379-0738(02)00086-5
  35. H. Pan, H. Han, S. Shan, and X. Chen, "Mean-Variance Loss for Deep Age Estimation from a Face," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 5285-5294, 2018.
  36. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition," arXiv Preprint arXiv:1409.1556, 2014.
  37. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 770-778, 2016.
  38. S. Rhyou, H. Kim, and K. Cha, “Development of Access Management System based on Face Recognition Using ResNet,” Journal of Korea Multimedia Society, Vol. 22, No. 8, pp. 823- 831, 2019. https://doi.org/10.9717/KMMS.2019.22.8.823
  39. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 2818-2826, 2016.
  40. J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L.F. Fe, "Imagenet: A Large-scale Hierarchical Image Database," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 248-255, 2009.