dropclust 0.0.5

A microscopical image and video platform applied to the analysis of levitating droplet clusters

DropClust

DropClust is an analytical tool that combines computer vision and machine learning algorithms to assess the morphology, geometry, and dynamics of frame videos of droplet clusters (or similar objects).
Feature extraction capabilities were also added for further visual representation of the results.

Developed by the InfoChemistry scientific center, part of ITMO University.

Installation

We suggest throught conda and pip (with python>=3.9).

1. Install Anaconda.
2. Open an anaconda prompt / command prompt which has conda for python 3 in the path.
3. For a new environment for CPU only, run:
   conda create -n dropclust 'python==3.9'
4. To activate the new environment, run conda activate dropclust
5. For NVIDIA GPUs, run:
   pip install torch torchvision
   We suggest to install CUDA 12.6
6. To install the latest PyPi release of Dropclust and its dependencies (see setup.py), run:
   pip install dropclust.

System requirements

Linux, Windows and Mac OS are supported for running the code. For running the graphical interface you will need a Mac OS later than Yosemite. At least 8GB of RAM is required to run the software. 16GB-32GB may be required for larger images. The software has been tested on Windows 10, Windows 11, Ubuntu 24.04, Manjaro and limitedly tested on Mac OS.

Features

We calculate the following metrics / algorithms:

• Subject counting (amount of subjects).
• Area of subject (𝜇𝑚²).
• Roundness (0.0 - 1.0), having 1.0 for a perfect circle.
• Relative center coordinates.
• Voronoi diagram based on the centers.
• Voronoi entropy, a measure of order/chaos in the cells' positions.
• Convex hull.
• Continuous symmetry measure (CSM).
• Subjects segmentation and clustering.
• Color classification.
• Subject detection + tracking.

General workflow

In order to obtain metrics from segmented cells, the initial stained images are merged into a single image and organized into sub folders to be processed. A cell segmentation procedure is performed using Cellpose, then we extract the metrics and finally we store the results in the form of images and CSV files.

How to use

Work in progress

Citation

Work in progress