English

Updating the VESICLE-CNN Synapse Detector

Computer Vision and Pattern Recognition 2017-11-01 v1 Quantitative Methods Machine Learning

Abstract

We present an updated version of the VESICLE-CNN algorithm presented by Roncal et al. (2014). The original implementation makes use of a patch-based approach. This methodology is known to be slow due to repeated computations. We update this implementation to be fully convolutional through the use of dilated convolutions, recovering the expanded field of view achieved through the use of strided maxpools, but without a degradation of spatial resolution. This updated implementation performs as well as the original implementation, but with a 600×600\times speedup at test time. We release source code and data into the public domain.

Cite

@article{arxiv.1710.11397,
  title  = {Updating the VESICLE-CNN Synapse Detector},
  author = {Andrew Warrington and Frank Wood},
  journal= {arXiv preprint arXiv:1710.11397},
  year   = {2017}
}

Comments

Submitted as two side extended abstract to NIPS 2017 workshop: BigNeuro 2017: Analyzing brain data from nano to macroscale

R2 v1 2026-06-22T22:31:01.075Z