lacss 0.5.1

Cell segmentation and tracking

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)

Installation

pip install --upgrade pip
pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install lacss

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