Search Results for author:
Found 5 papers, 3 papers with code
Fully-Automatic Synapse Prediction and Validation on a Large Data Set
This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections.
Large-Scale Electron Microscopy Image Segmentation in Spark
In this paper, we propose a novel strategy to apply such segmentation on very large datasets that exceed the capacity of a single machine.
Electron Microscopy Image Segmentation
Image Segmentation
+2
Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla
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 nanometer resolution required to resolve them.
Annotating Synapses in Large EM Datasets
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics.
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
In this paper, we propose a new strategy, focused proofreading, that works with automatic segmentation and aims to limit proofreading to the regions of a dataset that are most impactful to the resulting circuit.
