A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms

Ali, Rubbiya A., Landsberg, Michael J., Knauth, Emily, Morgan, Garry P., Marsh, Brad J. and Hankamer, Ben (2012) A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms. Plos One, 7 3: e33697-1-e33697-12.


Author Ali, Rubbiya A.
Landsberg, Michael J.
Knauth, Emily
Morgan, Garry P.
Marsh, Brad J.
Hankamer, Ben
Title A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms
Journal name Plos One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2012-03
Sub-type Article (original research)
DOI 10.1371/journal.pone.0033697
Volume 7
Issue 3
Start page e33697-1
End page e33697-12
Total pages 12
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2013
Language eng
Abstract 3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters-the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms.
Keyword Cryo-Em Micrographs
Edge-Detection
Resolution
Particles
Reconstructions
Bsoft
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number e33697. Published: March 29, 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
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