Tools for cell segmentation
Project description
LACSS
pip install lacss
LACSS is a deep-learning model for single-cell segmentation from microscopy images. See references below:
What's new (0.11)
You can now deploy the LACSS predictor as an 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 an interactive manner.
Why LACSS?
LACSS is designed to utilize point labels for model training. You have three options: (1) Label each cell with a single point, (2) label each cell with a single point and then label each image with a binary mask that covers all cells, or (3) Label each cell with a separate segmentation mask (as in standard supervised training). You can of course also combined these labels in any way you want.
Method | Data(left) / Label(right) |
---|---|
Point | |
Point + Mask | |
Segmentation |
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
Model Inference
Documentation
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