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Semantic Volume Segmentation

Where can I find certain meaningful structures in my biomedical image stacks?

Exploitation of Context Cues in Volumetric Data

 

[Sickert16:SVS]


Sven Sickert and Erik Rodner and Joachim Denzler. Semantic Volume Segmentation with Iterative Context Integration for Bio-medical Image Stacks. Pattern Recognition and Image Analysis (PRIA), 26(1), 197-204, 2016.  [bib]
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  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.