Electron microscopy

Drishti (from Sanskrit  word for "vision" or "insight") is a multi-platform, open-source volume-exploration and presentation tool. Written for visualizing tomography data, electron-microscopy data and the like.

Estimate the positions and spacing between sections (or at local points) of three dimensional image data. This method may be applied to any imaging modality that acquires 3-dimensional data as a stack of 2-dimensional sections. We provide plugins for both Fiji and TrakEM2.

Super-resolve anisotropic EM data along low-res axis with deep learning.

Multicut workflow for large connectomics data. Using luigi for pipelining and caching processing steps. Most of the computations are done out-of-core using hdf5 as backend and implementations from nifty

SuRVoS: Super-Region Volume Segmentation workbench

A volume is first partitioned into Super-Regions (superpixels or supervoxels) and then interactively segmented by the user providing training annotations. SuRVoS can then learn from and extend the annotations to the whole volume.