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.
Highlights
Electron tomography (ET) is an important tool for studying structural cell biology in situ by bridging the resolution gap between light microscopy and methods for protein structure determination at atomic resolution, such as X-ray and electron crystallography as well as nuclear magnetic resonance (NMR) spectroscopy
We present a full 3D implementation of the bilateral edge filter (3D bilateral edgedetection (BLE)), which importantly eliminates the need for manual s2 optimization
Similar to the 2D BLE filter, the 3D BLE filter first calculates the photometric score for each individual voxel in the context of the ‘processing window’ of the image volume being analysed
Summary
Electron tomography (ET) is an important tool for studying structural cell biology in situ by bridging the resolution gap between light microscopy and methods for protein structure determination at atomic resolution, such as X-ray and electron crystallography as well as nuclear magnetic resonance (NMR) spectroscopy. Classical edge-detection algorithms such as the Sobel [6], Prewitt [6], Laplacian of Gaussian [6] and Canny edge detectors [7] are increasingly being incorporated into semiautomated and automated methods for segmenting 3D image volumes. All of these are best suited to images with relatively high signal-to-noise ratios (SNR) and have limited use for the accurate/automated analysis of cellular tomograms, which have an inherently low SNR. A true 3D filter, capable of using data from adjacent slices, offers the advantage that additional information from either side of the ‘focal’ slice can be considered, thereby enabling enhanced noise suppression along with the detection of contiguous and legitimate structural details throughout the 3D image stack
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