26 citations · 28 across the 5 of their papers we have counts for
6 papers
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Randal Burns, Eric Perlman, Alex Baden +27
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visual…
Neural Reconstruction Integrity: A metric for assessing the connectivity of reconstructed neural networks
Elizabeth P. Reilly, Jeffrey S. Garretson, William Gray Roncal +4
Neuroscientists are actively pursuing high-precision maps, or graphs, consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs,…
Science In the Cloud (SIC): A use case in MRI Connectomics
Gregory Kiar, Krzysztof J. Gorgolewski, Dean Kleissas +8
Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught…
An Automated Images-to-Graphs Framework for High Resolution Connectomics
William Gray Roncal, Dean M. Kleissas, Joshua T. Vogelstein +8
Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have…
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes
Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri +8
In this paper, we present a new pipeline which automatically identifies and annotates axoplasmic reticula, which are small subcellular structures present only in axons. We run our…
The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience
Randal Burns, William Gray Roncal, Dean Kleissas +17
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for t…