Verena Kaynig

TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.

TL;DR: The Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically, is introduced.

TL;DR: In this paper, the authors describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many subcellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database.

TL;DR: In this paper, a random forest classifier is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image, which are then combined into geometrically consistent 3D objects by segmentation fusion.

TL;DR: This work proposes a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting using the probability output of a random forest classifier in a regular cost function, which enforces gap completion via perceptual grouping constraints.