Automated assessment of bone age using deep learning and Gaussian process regression
- Author
- Tom Van Steenkiste, Joeri Ruyssinck (UGent) , Olivier Janssens (UGent) , Baptist Vandersmissen, Florian Vandecasteele, Pieter Devolder (UGent) , Eric Achten (UGent) , Sofie Van Hoecke (UGent) , Dirk Deschrijver (UGent) and Tom Dhaene (UGent)
- Organization
- Abstract
- Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8580845
- MLA
- Van Steenkiste, Tom, et al. “Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression.” 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), IEEE, 2018, pp. 674–77, doi:10.1109/EMBC.2018.8512334.
- APA
- Van Steenkiste, T., Ruyssinck, J., Janssens, O., Vandersmissen, B., Vandecasteele, F., Devolder, P., … Dhaene, T. (2018). Automated assessment of bone age using deep learning and Gaussian process regression. 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 674–677. https://doi.org/10.1109/EMBC.2018.8512334
- Chicago author-date
- Van Steenkiste, Tom, Joeri Ruyssinck, Olivier Janssens, Baptist Vandersmissen, Florian Vandecasteele, Pieter Devolder, Eric Achten, Sofie Van Hoecke, Dirk Deschrijver, and Tom Dhaene. 2018. “Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression.” In 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 674–77. New York, NY, USA: IEEE. https://doi.org/10.1109/EMBC.2018.8512334.
- Chicago author-date (all authors)
- Van Steenkiste, Tom, Joeri Ruyssinck, Olivier Janssens, Baptist Vandersmissen, Florian Vandecasteele, Pieter Devolder, Eric Achten, Sofie Van Hoecke, Dirk Deschrijver, and Tom Dhaene. 2018. “Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression.” In 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 674–677. New York, NY, USA: IEEE. doi:10.1109/EMBC.2018.8512334.
- Vancouver
- 1.Van Steenkiste T, Ruyssinck J, Janssens O, Vandersmissen B, Vandecasteele F, Devolder P, et al. Automated assessment of bone age using deep learning and Gaussian process regression. In: 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). New York, NY, USA: IEEE; 2018. p. 674–7.
- IEEE
- [1]T. Van Steenkiste et al., “Automated assessment of bone age using deep learning and Gaussian process regression,” in 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Honolulu, HI, USA, 2018, pp. 674–677.
@inproceedings{8580845,
abstract = {{Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age.}},
author = {{Van Steenkiste, Tom and Ruyssinck, Joeri and Janssens, Olivier and Vandersmissen, Baptist and Vandecasteele, Florian and Devolder, Pieter and Achten, Eric and Van Hoecke, Sofie and Deschrijver, Dirk and Dhaene, Tom}},
booktitle = {{2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)}},
isbn = {{9781538636466}},
issn = {{1557-170X}},
language = {{eng}},
location = {{Honolulu, HI, USA}},
pages = {{674--677}},
publisher = {{IEEE}},
title = {{Automated assessment of bone age using deep learning and Gaussian process regression}},
url = {{http://doi.org/10.1109/EMBC.2018.8512334}},
year = {{2018}},
}
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