A workflow for the automatic segmentation of organelles in electron microscopy image stacks

Abstract

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime.

Visualizing viral protein structures in cells using genetic probes for correlated light and electron microscopy

“…Within the past 20 years, great progress has been made in EM probe development [26, 27, 35, 38], instrumentation [53, 54, 63], image acquisition [57, 64], image reconstruction [65, 66], and segmentation [67, 68]. In contrast to conventional EM stains in which most objects are not stained specifically, now we have a diverse set of powerful tools to identify a specific protein or structure in EM by localized oxidation of DAB into osmiophilic polymer that selectively contrasts the protein of interest, either through photo-oxidation or enzymatic-oxidation.…”

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

“…Kreshuk et al, 2011;Perez et al, 2014). Or, extended computing capacity on computer clusters is necessary for some algorithms (e.g.…”

“…Or, extended computing capacity on computer clusters is necessary for some algorithms (e.g. Perez et al, 2014). To overcome these obstacles, we developed a semi-automatic pipeline that does require neither image training nor large computer clusters.…”

Machine learning applications in cell image analysis

“…Although this review focuses on applications of light microscopy at the cellular level, some of the processing steps are similar in other imaging settings. For example, segmentation is also required for locating cell organelles in electron microscopy images 15 …”

“…For example, segmentation is also required for locating cell organelles in electron microscopy images. 15 Each processing stage poses a number of computational challenges. For example, cell segmentation can be difficult due to inherent variability in cell appearance; cell tracking can be hard in the presence of multiple overlapping objects; and categorisation of cells trajectories can be non-trivial due to varying lengths of tracks.…”