From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Wen-Ting Tsai; Ahmed Hassan; Purbasha Sarkar; Joaquin Correa; Zoltan Metlagel; Danielle M. Jorgens; Manfred Auer

doi:10.3791/51673

Journal

/

Bioengineering

/

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

JoVE Journal
Bioengineering

This content is Free Access.

JoVE Journal Bioengineering

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

24,350 Views

•

12:08 min

•

August 13, 2014

DOI:

10.3791/51673-v

DOI:

10.3791/51673-v

•

12:08 min

August 13, 2014

•

1 Views

Wen-Ting Tsai¹, Ahmed Hassan¹, Purbasha Sarkar², Joaquin Correa^1,3, Zoltan Metlagel¹, Danielle M. Jorgens¹, Manfred Auer^1,2

¹Life Sciences Division,Lawrence Berkeley National Laboratory, ²Joint Bioenergy Institute, Physical Biosciences Division,Lawrence Berkeley National Laboratory, ³National Energy Research Scientific Computing Center,Lawrence Berkeley National Laboratory

Summary

The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.