Efficient Epiphyses Localization Using Regression Tree Ensembles and a Conditional Random Field

Abstract

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.