@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}
}
@conference {Glocker2010,
	title = {TriangleFlow: Optical flow with triangulation-based higher-order likelihoods},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6313 LNCS},
	number = {PART 3},
	year = {2010},
	pages = {272{\textendash}285},
	abstract = {We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photo-consistency cost and a prior on affine motions both modeled in terms of higher-order potential functions. Reasoning jointly over a large set of unknown variables provides more reliable motion estimates and a robust matching criterion. One of the main contributions is that unlike previous region-based methods, we omit the assumption of constant flow. Instead, we consider local affine warps whose likelihood energy can be computed exactly without approximations. This results in a tractable, so-called, higher-order likelihood function. We realize this idea by employing triangulation meshes which immensely reduce the complexity of the problem. Optimization is performed by hierarchical fusion moves and an adaptive mesh refinement strategy. Experiments show that we achieve high-quality motion fields on several data sets including the Middlebury optical flow database. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {364215557X},
	issn = {16113349},
	doi = {10.1007/978-3-642-15558-1_20},
	url = {http://vision.middlebury.edu/flow/},
	author = {Glocker, Ben and Heibel, T. Hauke and Navab, Nassir and Kohli, Pushmeet and Carsten Rother}
}
