Paper Info
Title
A Multi-Pass Approach to Large-Scale Connectomics.
Abstract
The field of connectomics faces unprecedented big challenges. To reconstruct neuronal connectivity, automated pixel-level segmentation is required for petabytes of streaming electron microscopy data. Existing algorithms provide relatively good accuracy but are unacceptably slow, and would require years to extract connectivity graphs from even a single cubic millimeter of neural tissue. Here we present a viable real-time solution, a multi-pass pipeline optimized for shared-memory multicore systems, capable of processing data at near the terabyte-per-hour pace of multi-beam electron microscopes. The pipeline makes an initial fast-pass over the data, and then makes a second slow-pass to iteratively correct errors in the output of the fast-pass. We demonstrate the accuracy of a sparse slow-pass reconstruction algorithm and suggest new methods for detecting morphological errors. Our fast-pass approach provided many algorithmic challenges, including the design and implementation of novel shallow convolutional neural nets and the parallelization of watershed and object-merging techniques. We use it to reconstruct, from image stack to skeletons, the full dataset of Kasthuri et al. (463 GB capturing 120,000 cubic microns) in a matter of hours on a single multicore machine rather than the weeks it has taken in the past on much larger distributed systems.
Year
Venue
Field
2016
arXiv: Quantitative Methods
Graph,Connectomics,Segmentation,Petabyte,Computer science,Reconstruction algorithm,Bioinformatics,Artificial neural network,Multi-core processor,Big data
DocType
Volume
Citations 
Journal
abs/1612.02120
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Yaron Meirovitch1253.33
Alexander Matveev21107.75
Hayk Saribekyan310.70
David Budden416718.45
David Rolnick56510.53
Gergely Ódor6112.14
Seymour Knowles-Barley792.06
Thouis Raymond Jones800.34
Hanspeter Pfister95933340.59
Jeff W. Lichtman1013412.41
Nir Shavit113780244.84