Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data

Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data

Adi Suissa-Peleg, Daniel Haehn, Seymour Knowles-Barley, Verena Kaynig, Thouis R. Jones, Alyssa Wilson, Schalek R, Jeffery W. Lichtman, and Hanspeter Pfister.

(Cambridge Univ Press: Microscopy and Microanalysis, 2016.)

Connectomics is the study of the dense structure of the neurons in the brain and their synapses, providing new insights into the relation between braintextquoterights structure and its function. Recent advances in Electron Microscopy enable high-resolution imaging (4nm per pixel) of neural tissue at a rate of roughly 10 terapixels in a single day, allowing neuroscientists to capture large blocks of neural tissue in a reasonable amount of time. The large amounts of data require novel computer vision based algorithms and scalable software frameworks to process this data. We describe RhoANA, our dense Automatic Neural Annotation framework, which we have developed in order to automatically align, segment and reconstruct a 1mm3 brain tissue (~2 peta-pixels).

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