lacss 0.11.1

Tools for cell segmentation

LACSS

LACSS is a deep-learning model for single-cell segmentation from microscopy images.

References:

• IEEE TMI doi:10.1109/TMI.2023.3312988
• Communications Biology 6,232 (2023)

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

  • Supervised Training
  • With point label + mask
  • With point label only

• Inference

Documentation

API documentation