@inproceedings{Sickert14:SVS,
    type = {inproceedings},
    key = {Sickert14:SVS},
    title = {Semantic Volume Segmentation with Iterative Context Integration},
    author = {Sven Sickert and Erik Rodner and Joachim Denzler},
    booktitle = {Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW)},
    year = {2014},
    address = {Koblenz},
    editor = {Dietrich Paulus and Christian Fuchs and Detlev Droege},
    month = {12},
    pages = {220-225},
    publisher = {University of Koblenz-Landau},
    abstract = {Automatic recognition of biological structures like membranes or synapses is important to analyze organic processes and to understand their functional behavior. To achieve this, volumetric images taken by electron microscopy or computed tomography have to be segmented into meaningful regions. We are extending iterative context forests which were developed for 2D image data for image stack segmentation. In particular, our method s able to learn high order dependencies and import contextual information, which often can not be learned by conventional Markov random field approaches usually used for this task. Our method is tested for very different and challenging medical and biological segmentation tasks.},
    groups = {semanticsegmentation,biomedical,3dsemseg},
    shorttitle = {OGRW 2014},
    url = {https://userpages.uni-koblenz.de/~agas/ogrw2014/bib.html},
}