htem / raygun

An image enhancement and segmentation pipeline for generating connectomic reconstructions from X-ray holographic nanotomography, using CycleGANs, Local Shape Descriptors, and Mutex Watershed. Built with PyTorch, Daisy, and Gunpowder.

Raygun

The goal of this repository is to provide an extendable toolbox for large-scale experimentation with deep learning techniques for image enhancement and segmentation of N-dimensional biological imaging data. It is designed to support high-performance computing clusters and utilize GPU-acceleration.

Training datasets, as well as model checkpoints used in the paper will be made accessible soon!

Install:

Run the following -->

conda create -n raygun python=3.9 tensorflow pytorch torchvision torchaudio cudatoolkit=11.3 affogato -c pytorch -c nvidia -c conda-forge 
conda activate raygun
pip install git+https://github.com/htem/raygun

Should you run into gcc / boost errors when conda/pip installing raygun, try installing libboost first:

sudo apt-get update
sudo apt-get install libboost-all-dev

Example train:

raygun-train path/to/train_config.json