Literature DB >> 19116188

The BoneXpert method for automated determination of skeletal maturity.

Hans Henrik Thodberg1, Sven Kreiborg, Anders Juul, Karen Damgaard Pedersen.   

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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Year:  2009        PMID: 19116188     DOI: 10.1109/TMI.2008.926067

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  72 in total

1.  A fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment.

Authors:  Chi-Wen Hsieh; Tzu-Chiang Liu; Tai-Lang Jong; Chui-Mei Tiu
Journal:  Med Biol Eng Comput       Date:  2010-04-20       Impact factor: 2.602

2.  Clinical application of automated Greulich-Pyle bone age determination in children with short stature.

Authors:  David D Martin; Dorothee Deusch; Roland Schweizer; Gerhard Binder; Hans Henrik Thodberg; Michael B Ranke
Journal:  Pediatr Radiol       Date:  2009-03-31

3.  MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.

Authors:  Simukayi Mutasa; Peter D Chang; Carrie Ruzal-Shapiro; Rama Ayyala
Journal:  J Digit Imaging       Date:  2018-08       Impact factor: 4.056

4.  Web-based bone age assessment by content-based image retrieval for case-based reasoning.

Authors:  Benedikt Fischer; Petra Welter; Rolf W Günther; Thomas M Deserno
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-06-14       Impact factor: 2.924

5.  A Deep Automated Skeletal Bone Age Assessment Model with Heterogeneous Features Learning.

Authors:  Chao Tong; Baoyu Liang; Jun Li; Zhigao Zheng
Journal:  J Med Syst       Date:  2018-11-03       Impact factor: 4.460

Review 6.  Imaging in Short Stature and Bone Age Estimation.

Authors:  Arun Kumar Gupta; Manisha Jana; Atin Kumar
Journal:  Indian J Pediatr       Date:  2019-03-19       Impact factor: 1.967

7.  Bone age determination in eutrophic, overweight and obese Brazilian children and adolescents: a comparison between computerized BoneXpert and Greulich-Pyle methods.

Authors:  Thiago O Artioli; Matheus A Alvares; Vanessa S Carvalho Macedo; Tatiane S Silva; Roberto Avritchir; Cristiane Kochi; Carlos A Longui
Journal:  Pediatr Radiol       Date:  2019-05-31

8.  Automated determination of bone age from hand X-rays at the end of puberty and its applicability for age estimation.

Authors:  Hans Henrik Thodberg; Rick R van Rijn; Oskar G Jenni; David D Martin
Journal:  Int J Legal Med       Date:  2016-10-18       Impact factor: 2.686

9.  Validation of automatic bone age determination in children with congenital adrenal hyperplasia.

Authors:  David D Martin; Katharina Heil; Conrad Heckmann; Angelika Zierl; Jürgen Schaefer; Michael B Ranke; Gerhard Binder
Journal:  Pediatr Radiol       Date:  2013-10-05

10.  A paediatric bone index derived by automated radiogrammetry.

Authors:  H H Thodberg; R R van Rijn; T Tanaka; D D Martin; S Kreiborg
Journal:  Osteoporos Int       Date:  2009-11-24       Impact factor: 4.507

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