Tools for cell segmentation
Project description
LACSS
LACSS is a deep-learning model for single-cell segmentation from microscopy images.
References:
What's new (0.11)
GRPC server
Lacss now comes with a GRPC server:
python -m lacss.deploy.remote_server --modelpath=<model_file_path>
For a GUI client see the Trackmate-Lacss project, which provides a FIJI/ImageJ plugin to perform cell segmentation/tracking in a interactive manner.
Installation
pip install lacss
For more details, see documentation
Why LACSS?
LACSS is designed to utilize point labels for model training. You have three options:
Method | Data(left) / Label(right) |
---|---|
Point | |
Point + Mask | |
Segmentation |
You can of course also combined these labels in any way you want.
What is included?
- A library for training LACSS model and performing inference
- A few pretrained models as transfer learning starting point
- SMC-based cell tracking utility for people interested in cell tracking
How to generate point label?
If your data include nuclei counter-stain, the easist way to generate point label for your image is to use a blob detection algorithm on the nuclei images:
Give it a try:
-
Model training
Documentation
Project details
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