<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richmond, David</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Glocker, Ben</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Uncertainty-driven forest predictors for vertebra localization and segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">9349</style></volume><pages><style face="normal" font="default" size="100%">653–660</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>