Uncertainty-driven forest predictors for vertebra localization and segmentation

TitleUncertainty-driven forest predictors for vertebra localization and segmentation
Publication TypeIn Collection
Year of Publication2015
AuthorsRichmond, D, Kainmueller, D, Glocker, B, Rother, C, Myers, G
Collection TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication Languageeng
ISSN Number16113349

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.

Citation KeyRichmond2015