Accurate localization of sets of anatomical landmarks is a challenging task, yet often required in automatic analysis of medical images. Several groups – e.g., Donner et al. – have shown that it is beneficial to incorporate geometrical relations of landmarks into detection procedures for complex anatomical structures. In this paper, we present a two-step approach (compared to three steps as suggested by Donner et al.) combining regression tree ensembles with a Conditional Random Field (CRF), modeling spatial relations. The comparably simple combination achieves a localization rate of 99.6 % on a challenging hand radiograph dataset showing high age-related variability, which is slightly superior than state-of-the-art results achieved by Hahmann et al.
CITATION STYLE
Mader, A. O., Schramm, H., & Meyer, C. (2017). Efficient epiphyses localization using regression tree ensembles and a conditional random field. In Informatik aktuell (pp. 179–184). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-662-54345-0_42
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