Ben Glocker, Johannes Feulner, Antonio Criminisi, David R. Haynor, and Ender Konukoglu
This paper presents a new method for automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. No assumptions are made about which section of the spine is visible or to which extent. Thus, our approach is more general than previous work while being computationally efficient.
Our algorithm is based on regression forests and probabilistic graphical models. The discriminative, regression part aims at roughly detecting the visible part of the spine.
Accurate localization and identification of individual vertebrae is achieved through a generative model capturing spinal shape and appearance. The system is evaluated quantitatively on 200 CT scans, the largest dataset reported for this purpose. We obtain an overall median localization error of less than 6 mm, with an identification rate of 81%.
[The pdf is a modified version of the original publication. Typos in Eq. 1 and 3 have been corrected.]
In MICCAI 2012 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention