34 citations · 108 across the 9 of their papers we have counts for
10 papers
Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes
Gary B Huang, Huei-Fang Yang, Shin-ya Takemura +2
We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation…
Fully-Automatic Synapse Prediction and Validation on a Large Data Set
Gary B. Huang, Louis K. Scheffer, Stephen M. Plaza
Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this inf…
Large-Scale Electron Microscopy Image Segmentation in Spark
Stephen M. Plaza, Stuart E. Berg
The emerging field of connectomics aims to unlock the mysteries of the brain by understanding the connectivity between neurons. To map this connectivity, we acquire thousands of el…
Large-scale EM Analysis of the Drosophila Antennal Lobe with Automatically Computed Synapse Point Clouds
Ting Zhao, Shin-ya Takemura, Gary B. Huang +7
The promise of extracting connectomes and performing useful analysis on large electron microscopy (EM) datasets has been an elusive dream for many years. Tracing in even the smalle…
Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla
Ting Zhao, Stephen M Plaza
Mapping the connectivity of neurons in the brain (i.e., connectomics) is a challenging problem due to both the number of connections in even the smallest organisms and the nanomete…
Identifying Synapses Using Deep and Wide Multiscale Recursive Networks
Gary B. Huang, Stephen Plaza
In this work, we propose a learning framework for identifying synapses using a deep and wide multi-scale recursive (DAWMR) network, previously considered in image segmentation appl…
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
Stephen M. Plaza
Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming,…
Annotating Synapses in Large EM Datasets
Stephen M. Plaza, Toufiq Parag, Gary B. Huang +3
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microsc…
Small Sample Learning of Superpixel Classifiers for EM Segmentation- Extended Version
Toufiq Parag, Stephen Plaza, Louis Scheffer
Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement…
A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation
Toufiq Parag, Anirban Chakraborty, Stephen Plaza +1
Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerat…