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.
Highlights
Advances in instrumentation for 3D Electron microscopy (EM) are fueling a renaissance in the study of quantitative neuroanatomy (Peddie and Collinson, 2014)
Segmentations generated with the maximum entropy algorithm (Figure 5C, recall = 0.992, precision = 0.498, F = 0.670, accuracy = 0.948) and Otsu’s singlelevel method (Figure 5D, recall = 0.958, precision = 0.687, F = 0.812, accuracy = 0.977) retain elements of the Golgi apparatus as false positives
With that goal in mind, this study aimed to develop a method for the accurate automatic segmentation of organelles in EM image stacks that: (1) could be adapted to any organelle of interest, and (2) could be applied to teravoxel-sized datasets in a computationally efficient manner
Summary
Advances in instrumentation for 3D EM are fueling a renaissance in the study of quantitative neuroanatomy (Peddie and Collinson, 2014). Harnessing the power of these emerging 3D techniques to study the structure of whole cell organellomes is of critical importance to the field of neuroscience. Abnormal organelle morphologies and distributions within cells of the nervous system are characteristic phenotypes of a growing number of neurodegenerative diseases. Aberrant mitochondrial fragmentation is believed to be an early and key event in neurodegeneration (Knott et al, 2008; Campello and Scorrano, 2010), and changes in mitochondrial structure have been observed in Alzheimer’s disease (AD) neurons from human biopsies (Hirai et al, 2001; Zhu et al, 2013). Altered nuclear or nucleolar morphologies have been observed in a host of pathologies, including AD (Mann et al, 1985; Riudavets et al, 2007), torsion dystonia, (Kim et al, 2010), and Lewy body dementia (Gagyi et al, 2012)
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