Abstract: The olfactory bulb (OB) as part of the olfactory pathway plays a central role in odor perception. Several studies have already established a connection between an olfactory impairment and the occurrence of neurodegenerative diseases (Parkinson's disease, Alzheimer's disease, etc.). This impairment is often detectable years before further symptoms. Moreover, it is connected to a volume loss of the OB. Therefore, in future the volume of the OB could contribute as a marker for detection and diagnosis of such diseases. Despite this great importance, there is currently no standard procedure for the volumetric analysis of the OB and above all no objective investigator-independent measurement methods.

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 computer tomography have to be segmented into meaningful semantic regions. We are extending iterative context forests which were developed for 2D image data to image stack segmentation. In particular, our method is 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 on very different and challenging medical and biological segmentation tasks.

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.