tbepler/topaz
Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.
This project helps cryo-electron microscopy (cryo-EM) researchers accurately identify individual protein particles within noisy microscopic images and 3D tomograms. You input raw cryo-EM images or tomograms, and it outputs precise locations of particles of interest. This is ideal for structural biologists and biochemists working to determine molecular structures from cryo-EM data.
208 stars.
Use this if you need to reliably find and extract protein particles from noisy cryo-EM micrographs or denoise your microscopy data for clearer analysis.
Not ideal if you are not working with cryo-electron microscopy data or do not have access to an Nvidia GPU.
Stars
208
Forks
71
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jan 27, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/tbepler/topaz"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
ProjectNeura/MIPCandy
Build a complete experiment pipeline for your PyTorch MIP model in 10 seconds.
canlab/CanlabCore
Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is...
MPI-Dortmund/tomotwin-cryoet
cryo-ET particle picking by representation and metric learning
bioimage-io/core-bioimage-io-python
Python libraries for loading, running and packaging bioimage.io models
sunhongfu/deepMRI
MRI reconstruction (e.g., QSM) using deep learning methods