@incollection {Richmond2015,
	title = {Uncertainty-driven forest predictors for vertebra localization and segmentation},
	volume = {9349},
	year = {2015},
	pages = {653{\textendash}660},
	abstract = {Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.},
	issn = {16113349},
	doi = {10.1007/978-3-319-24553-9_80},
	author = {Richmond, David and Kainmueller, Dagmar and Glocker, Ben and Carsten Rother and Myers, Gene}
}
