Object-based representation and analysis of light and electron microscopic volume data using Blender

BMC Bioinformatics

Research Article

Markus Conzelmann¹  Elizabeth A. Williams¹  Aurora Panzera¹  Gáspár Jékely¹  Albina Asadulina¹ 

[1] Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany;

关键词: Platynereis;    Gene expression atlas;    Connectome;    Surface representation;    3D model;    Blender;   

DOI  :  10.1186/s12859-015-0652-7

 received in 2014-12-05, accepted in 2015-06-29,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundRapid improvements in light and electron microscopy imaging techniques and the development of 3D anatomical atlases necessitate new approaches for the visualization and analysis of image data. Pixel-based representations of raw light microscopy data suffer from limitations in the number of channels that can be visualized simultaneously. Complex electron microscopic reconstructions from large tissue volumes are also challenging to visualize and analyze.ResultsHere we exploit the advanced visualization capabilities and flexibility of the open-source platform Blender to visualize and analyze anatomical atlases. We use light-microscopy-based gene expression atlases and electron microscopy connectome volume data from larval stages of the marine annelid Platynereis dumerilii. We build object-based larval gene expression atlases in Blender and develop tools for annotation and coexpression analysis. We also represent and analyze connectome data including neuronal reconstructions and underlying synaptic connectivity.ConclusionsWe demonstrate the power and flexibility of Blender for visualizing and exploring complex anatomical atlases. The resources we have developed for Platynereis will facilitate data sharing and the standardization of anatomical atlases for this species. The flexibility of Blender, particularly its embedded Python application programming interface, means that our methods can be easily extended to other organisms.

【 授权许可】

Unknown   
© Asadulina et al. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.