![]() ![]() The NCR Toolset did not include a viewer. After capturing the raw data we used the NCR Toolset (publicly available at ) to calculate transformations for positioning tiles into canonical images, and for aligning images as a volume. The development of Viking began shortly before the capture of the RC1 data set and it was ready for use upon completion of capture. We present our solution, Viking, in the context of our retinal connectivity work tracing the AII amacrine cell of the retina. Browsing and annotating the myriad features of such massive data sets, as well as summarizing attributes and relationships in human-understandable forms, are substantial computational undertakings. Our lab focuses on retinal networks and our first small neural volume (RC1) required capturing large amounts of serial section transmission electron microscopy data ( Anderson et al., 2009): about 341 000 images, at 16 megapixels each. Axons can extend from 10 to 10 6 μm dendritic arbors can subtend less than 10 μm or over 1000 μm single cells may make one or thousands of connections and interact with 1–12 different classes of target cells individual synapses and gap junctions subtend 0.1–1 μm, and anatomical pairings can be validated only with resolutions of 2 nm or better. Neural features span six to nine orders of magnitude. ![]() The utility of this ability is typified by descriptive studies of neural connectivity, which is the assembly of neural connectomes. (5) It has an easily extensible user interface, allowing addition of specialized modules without rewriting the viewer.Īutomated microscopy systems, in combination with automated registration algorithms, are allowing microscopists to collect data sets of unprecedented scale. (4) It is capable of applying transformations in real-time. (3) It cleanly demarcates viewing and analysis from data collection and hosting. (2) It supports a multi-user, collaborative annotation strategy. (1) It works over the internet using HTTP and supports many concurrent users limited only by hardware. The Viking application was our solution created to view and annotate a 16.5 TB ultrastructural retinal connectome volume and we demonstrate its utility in reconstructing neural networks for a distinctive retinal amacrine cell class. Finally annotated anatomical data sets can represent a significant investment of resources and should be easily accessible to the scientific community. Large data sets quickly exceed an individual's capability for timely analysis and present challenges in efficiently applying transforms, if needed. The cost and overhead of collecting and storing the data can be extremely high. These large data sets present significant challenges for data storage, access, viewing, annotation and analysis. Modern microscope automation permits the collection of vast amounts of continuous anatomical imagery in both two and three dimensions. ![]()
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