Region of Interest Localization for Bone Age Estimation Using Whole-Body Bone Scintigraphy
1
조회
0
다운로드
2
인용
0
참고문헌
키워드
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.
Abstract
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.
참고문헌 (26)
- D. W. Belsky, "Quantification of Biological Aging in Young Adults", Proceedings of the National Academy of Sciences, 112(30), pp.E4104~E4110 (2015)
- N. Barzilai, "The place of genetics in ageing research", Nature Reviews Genetics, 13(8), pp.589~594 (2012)
- H. Heyn, "Distinct DNA methylomes of newborns and centenarians", Proc Natl Acad Sci USA, 109(26), pp.10522~10527 (2012), University of Southern California
- Young Gon Kang, "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, 13, pp.429~436 (2018)
- G. A. Borkan, "Assessment of biological age using a profile of physical parameters", Journal of Gerontology, 35(2), pp.177~184 (1980)
- M. Uttley, "Efficacy of a composite biological age score to predict ten-year survival among Kansas and Nebraska Mennonites", Human Biology, 66(1), pp.121~144 (1994)
- W.W. Greulich, "Radiographic atlas of skeletal development of hand wrist" (1959), Stanford University Press
- J.M. Tanner, "Assessment of skeletal maturity and prediction of adult height (TW3 method)" (2001), Saunders Company
- H. H. Thodberg, "The BoneXpert method for automated determination of skeletal maturity", IEEE Transactions on Medical Imaging, IEEE, 28(1), pp.52~66 (2009)
- Jang Hyung Lee, "Bone age estimation using deep learning and hand X-ray images", Biomedical Engineering Letters (BMEL), 10(3), pp.323~331 (2020), 대한의용생체공학회[DOI]
- W. Brenner, "Age and sex-related bone uptake of Tc-99m-HDP measured by whole-body bone scanning", Nuclear medicine, 39(5), pp.127~132 (2000)
- V. D. Kakhki, "Age-related normal variants of sternal uptake on bone scintigraphy", Clinical Nuclear Medicine, 31(2), pp.63~67 (2006)
- Phap Do Cong Nguyen, "Multiple Inputs Deep Neural Networks for Bone Age Estimation Using Whole-Body Bone Scintigraphy", 멀티미디어학회논문지, 22(12), pp.1376~1384 (2019), 한국멀티미디어학회[DOI]
- T. V. D. Wyngaert, "The EANM Practice Guidelines for Bone Scintigraphy", European Journal of Nuclear Medicine and Molecular Imaging, 43(9), pp.1723~1738 (2016)
- M. Escobar, "Hand pose estimation for pediatric bone age assessment", International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.531~539 (2019)
- Y. Wang, "Application of Deep learning in Bone age assessment", IOP Conf. Series: Earth Environmental Science, 199(3) (2018)
- M. W. Nadeem, "Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions. Diagnostics", Diagnostics (Basel), 10(781) (2020)
- C. Spampinato, "Deep learning for automated skeletal bone age assessment in x-ray images", Medical image analysis, 36, pp.41~51 (2017)
- V. I. Iglovikov, "Paediatric bone age assessment using deep convolutional neural networks", Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 11045, pp.300~308 (2018)
- H. Lee, "Fully Automated Deep Learning System for Bone Age Assessment", Journal of Digital Imaging, 30(4), pp.427~441 (2017)
- J. Li, "An attention-based multi-resolution model for prostate whole slide image classification and localization", arXiv:1905.13208, ~ (2019)
- J. Fu, "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 (2017)
- B. Zhou, "Learning deep features for discriminative localization", Proceedings of the IEEE conference on computer vision and pattern recognition, 1, pp.2921~2929 (2016)
- R. R. Selvaraju, "Grad-cam : Visual explanations from deep networks via gradient-based localization", Proceedings of the IEEE international conference on computer vision, pp.618~626 (2017)
- K. He, "Deep residual learning for image recognition", IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 1, pp.770~778 (2016)
- M. A. Zulkifley, "Automated Bone Age Assessment with Image Registration Using Hand X-ray Images", Appl. Sci, 10(7233) (2020)