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01.01.2015 | Software Original Article | Ausgabe 1/2015 Open Access

Neuroinformatics 1/2015

NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks

Zeitschrift:
Neuroinformatics > Ausgabe 1/2015
Autoren:
Anne Jorstad, Biagio Nigro, Corrado Cali, Marta Wawrzyniak, Pascal Fua, Graham Knott
Wichtige Hinweise

Information Sharing Statement

All code for the NeuroMorph Toolkit (RRID:SciRes_000156) is available in open source under the GNU General Public License as published by the Free Software Foundation, and is available for download at cvlab.​epfl.​ch/​NeuroMorph. Example meshes and detailed documentation are also provided at this link. The toolkit functions as an add-on within the Blender open source modeling software (RRID:nif-0000-31943), available at www.​blender.​org.

Abstract

Serialelectron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.

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