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Found 13 papers, 5 papers with code
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries.
Automated Neuron Shape Analysis from Electron Microscopy
Morphology based analysis of cell types has been an area of great interest to the neuroscience community for several decades.
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images.
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy.
Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets
The tasks are executed by local and cloud GPUs and CPUs.
Synaptic Partner Assignment Using Attentional Voxel Association Networks
The network takes the local image context and a binary mask representing a single cleft as input.
Reconstructing neuronal anatomy from whole-brain images
Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system.
An Error Detection and Correction Framework for Connectomics
Both tasks take as input the raw image and a binary mask representing a candidate object.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs
Other things being equal, processing a larger image tends to increase throughput, because fractionally less computation is wasted on the borders of the image.
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines
Applying Brent's theorem to the task dependency graph implies that linear speedup with the number of processors is attainable within the PRAM model of parallel computation, for wide network architectures.
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.
