A semantic and instance segmentation toolbox for EM connectomics.
The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individual synapses. Recent advances in electronic microscopy (EM) have enabled the collection of a large number of image stacks at nanometer resolution, but the annotation requires expertise and is super time-consuming. Here we provide a deep learning framework powered by PyTorch for automatic and semi-automatic image segmentation in connectomics.
An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
A web-based interactive tool for training deep neural networks for segmentation tasks. It consists of a server backend that runs on a high-performance GPU compute node, and a front end user interface that runs on a web browser. User configures a classifier, the classes of objects to be detected, and the set of images to use for training and validation.
Efficient Compression of Segmentation Data For Connectomics
Compresso is a new compression scheme for label data that outperforms existing approaches by using a sliding window to exploit redundancy across border regions in 2D and 3D.
Source code: https://github.com/VCG/compresso