The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
The BoneXpert Method for Automated Determination of Skeletal Maturity
Abstract
Bone age rating is associated with a considerable variability from the human interpretation, and this is the motivation for presenting a new method for automated determination of bone age (skeletal maturity). The method, called BoneXpert, reconstructs, from radiographs of the hand, the borders of 15 bones automatically and then computes "intrinsic" bone ages for each of 13 bones (radius, ulna, and 11 short bones). Finally, it transforms the intrinsic bone ages into Greulich Pyle (GP) or Tanner Whitehouse (TW) bone age. The bone reconstruction method automatically rejects images with abnormal bone morphology or very poor image quality. From the methodological point of view, BoneXpert contains the following innovations: 1) a generative model (active appearance model) for the bone reconstruction; 2) the prediction of bone age from shape, intensity, and texture scores derived from principal component analysis; 3) the consensus bone age concept that defines bone age of each bone as the best estimate of the bone age of the other bones in the hand; 4) a common bone age model for males and females; and 5) the unified modelling of TW and GP bone age. BoneXpert is developed on 1559 images. It is validated on the Greulich Pyle atlas in the age range 2-17 years yielding an SD of 0.42 years [0.37; 0.47] 95% conf, and on 84 clinical TW-rated images yielding an SD of 0.80 years [0.68; 0.93] 95% conf. The precision of the GP bone age determination (its ability to yield the same result on a repeated radiograph) is inferred under suitable assumptions from six longitudinal series of radiographs. The result is an SD on a single determination of 0.17 years [0.13; 0.21] 95% conf.
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Cites
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Cited by
Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V., & Langlotz, C. P. (2018) · Radiology · doi:10.1148/radiol.2017170236 · Cited by 456
Fully Automated Deep Learning System for Bone Age Assessment
Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9955-8 · Cited by 452
Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306
Galsgaard, A., Doorschodt, T., Holten, A.-L., Müller, F. C., Ploug Boesen, M., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110231 · Cited by 38
Dallora, A. L., Kvist, O., Berglund, J. S., Ruiz, S. D., Boldt, M., Flodmark, C.-E., & Anderberg, P. (2020) · JMIR Medical Informatics · doi:10.2196/18846 · Cited by 13
Metadata
- Authors
- Thodberg, H. H., Kreiborg, S., Juul, A., & Pedersen, K. D.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2009-01
- Volume
- 28
- Issue
- 1
- Pages
- 52-66
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2162206900
- Cited by
- 438
- References
- 13
- FWCI
- 144.10
- Type
- journal article
- Language
- en
- Open access
- closed
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