ZauggGroup/DeePiCt
Pipeline for the automatic detection and segmentation of particles and cellular structures in 3D Cryo-ET data, based on deep learning (convolutional neural networks).
This project helps cryo-electron tomography (cryo-ET) researchers quickly identify and outline specific particles and cellular structures within their 3D image data. You input your cryo-ET tomograms, and it outputs segmented images highlighting structures like organelles, membranes, ribosomes, or fatty-acid synthases. This is for scientists analyzing the intricate details of cellular environments.
No commits in the last 6 months.
Use this if you are a life scientist or microscopist who generates large amounts of 3D cryo-ET data and needs an automated way to pinpoint and segment molecular patterns within crowded cellular contexts.
Not ideal if you are working with 2D microscopy images or need to analyze structures outside of cellular cryo-ET data.
Stars
41
Forks
12
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ZauggGroup/DeePiCt"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
ProjectNeura/MIPCandy
Build a complete experiment pipeline for your PyTorch MIP model in 10 seconds.
tbepler/topaz
Pipeline for particle picking in cryo-electron microscopy images using convolutional neural...
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