PyTorch Connectomics

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.

Website: https://zudi-lin.github.io/pytorch_connectomics/build/html/index.html
Source code: https://github.com/zudi-lin/pytorch_connectomics

Peax

Interactive visual pattern search in sequential data using unsupervised deep representation learning

Peax is a novel feature-based technique for interactive visual pattern search in sequential data based on a convolutional autoencoder for unsupervised representation learning of regions in sequential data. Peax enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity, for which an active learning strategy is employed to focus the labeling process on useful regions for training a classifier. ScreenIt has been developed in collaboration with Novartis Institute for Biomedical Research.

Website: http://peax.lekschas.de
Source code: https://github.com/novartis/peax

ICON

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.

Website: http://rhoana.org/icon
Source code: http://rhoana.org/icon

RhoANA

Dense Automatic Neural Annotation

RhoANA is software for dense automatic annotation of neurons in EM serial sections. It includes a processing pipeline, as well as Mojo, a proofreading and annotation tool.

Website: http://www.rhoana.org
Source code: https://github.com/Rhoana

Skin Reflectance Database

The MERL / ETH Skin Reflectance Database is a set of statistics for parameters of the Torrance-Sparrow and Blinn-Phong analytic BRDF models and face albedo. We derived these statistics from measuring skin reflectance of 149 subjects of varying age, gender, and race. We are making the collected statistics publicly available to the research community for applications in face synthesis and analysis.

Website: https://vcglab.org/facescanning/