Python package for segmentation and analysis of Cryo-ET
-
Updated
Dec 3, 2017 - Python
Python package for segmentation and analysis of Cryo-ET
Python scripts to quantiatively describe Arp2/3 complex organization on travelling Actin Waves
Python scritps for rendering and distance analysis of proteins (proteasome) and segmentations (poly-GA aggregates) in Cryo-ET
PyTorch implementation of "Open-set Recognition of Unseen Macromolecules in Cellular Electron Cryo-Tomograms by Soft Large Margin Centralized Cosine Loss"
A tool to normalize CryoET data by matching amplitude spectrums.
Toolbox for post-correlation cryo-CLEM workflow developed at Chlanda Lab, Heidelberg University.
Denoising and segmentation networks for cryoET based on U-net architecture implemented in Pytorch
Electron tomography toolkit: geometric picking and sub-volume averaging
2D NN-based particle picking from sparse labels
Pipeline for the automatic detection and segmentation of particles and cellular structures in 3D Cryo-ET data, based on deep learning (convolutional neural networks).
TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
Add a description, image, and links to the cryo-et topic page so that developers can more easily learn about it.
To associate your repository with the cryo-et topic, visit your repo's landing page and select "manage topics."