EspINA is a user-friendly tool which performs segmentation and analysis of elements in a reconstructed 3D volume of the cerebral cortex, and greatly facilitates and accelerates these processes. It allows visualization and segmentation of large image stacks datasets, both from electron micrsocopy (e.g. FIB/SEM) and confocal laser microscopy.
Fluent navigation through the different sections of the stack using three planar views. Simultaneous different channel visualization can be performed in a customized stainings and intensity/contrast properties. Navigation bookmarks helps to easily find stack spots.
Identify as many elements as you want using our classification or expand it as needed. Reconstruct them using our manual or semi-automated tools.
If the result of the segmentation is not completely satisfactory, we provide a set of tools to enhance their morphology automatically or manually.
Geometrical features of segmented elements are relevant to their function. Different 3D visualizations provide detailed views for every segmented element.
The final step of the working session is the extraction of measurements for each object segmented. A list of morphometric features and parameters can be selected to be easily exported to standard .csv or .xls spreadsheet format.
New tools, visualizations and reports can be added via our plugin system. You can even add your own.
You can download Ubuntu binaries from our PPA.
Current version is only available for Linux platforms.
Download our FIB/SEM stack sample from mouse brain to start testing out EspINA without delay.
Join our Google+ page and become part of our user and developer community.
EspINA software calculates the number of synapses per unit volume to analyze 3D samples reconstructed from serial sections obtained from brain tissue.
Specific software tools allowed us to determine synapse position and to analyze their spatial distribution using spatial statistical methods.
We examined the cerebral cortex of Alzheimer's disease patients to find new aspects of the pathological process associated with the disease.
Efficient computational technique to automatically extract the synaptic apposition surfaces of three-dimensionally reconstructed synapses.
Description of a method that works with anisotropic voxels and that is computationally efficient allowing the automatic segmentation of large image stacks.
Efficient, complete and automatic 3D reconstruction of identified dendrites, including their spines and synapses, from GFP/DAB-labeled neurons, with a resolution comparable to that of TEM.
If you have used EspINA to get your results and you have published them (congratulations!!) please, let us know and your publication will also appear here. Reviews of image processing software which include EspINA are also welcome.