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 title = {Towards Automatic Bone Age Estimation from MRI: Localization of 3D Anatomical Landmarks},
 type = {inBook},
 year = {2014},
 identifiers = {[object Object]},
 pages = {421-428},
 volume = {17},
 websites = {http://link.springer.com/10.1007/978-3-319-10470-6_53},
 publisher = {Springer, Cham},
 city = {Boston},
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 abstract = {Bone age estimation (BAE) is an important procedure in forensic practice which recently has seen a shift in attention from X-ray to MRI based imaging. To automate BAE from MRI, localization of the joints between hand bones is a crucial first step, which is challenging due to anatomical variations, different poses and repeating structures within the hand. We propose a landmark localization algorithm using multiple random regression forests, first analyzing the shape of the hand from information of the whole image, thus implicitly modeling the global landmark configuration, followed by a refinement based on more local information to increase prediction accuracy. We are able to clearly outperform related approaches on our dataset of 60 T1-weighted MR images, achieving a mean landmark localization error of 1.4 ?? 1.5mm, while having only 0.25% outliers with an error greater than 10mm.},
 bibtype = {inBook},
 author = {Ebner, Thomas and Stern, Darko and Donner, Rene and Bischof, Horst and Urschler, Martin},
 book = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}
}

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