ilastik: interactive machine learning for (bio)image analysis.

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier.

Neuron Segmentation With High-Level Biological Priors.

We present a novel approach to the problem of neuron segmentation in image volumes acquired by an electron microscopy. Existing methods, such as agglomerative or correlation clustering, rely solely on boundary evidence and have problems where such an evidence is lacking (e.g.

Attention-network specific alterations of structural connectivity in the undamaged white matter in acute neglect.

Visual neglect results from dysfunction within the spatial attention network. The structural connectivity in undamaged brain tissue in neglect has barely been investigated until now. In the present study, we explored the microstructural white matter characteristics of the contralesional hemisphere in relation to neglect severity and recovery in acute stroke patients.