Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data

Citation:

Suissa-Peleg A, Haehn D, Knowles-Barley S, Kaynig V, Jones TR, Wilson A, Schalek R, Lichtman JW, Pfister H. Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data [Internet]. In: Microscopy and Microanalysis. 2016 p. 536-537.
Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data

Date Presented:

28 July

Abstract:

Connectomics is the study of the dense structure of the neurons in the brain and their synapses, providing new insights into the relation between brain’s 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).

Publisher's Version

Last updated on 11/04/2016