georgeoshardo/SyMBac
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.
This tool helps cell biologists and microbiologists accurately segment bacteria in microscope images without manually creating training data. It generates realistic, synthetic phase contrast or fluorescence images of bacteria, especially those growing in mother machine devices, that can be used to train machine learning models for image analysis. The primary users are researchers working with bacterial imaging who need high-quality, diverse training datasets.
Available on PyPI.
Use this if you need unlimited, high-quality synthetic bacterial microscopy images to train your machine learning models for accurate cell segmentation, especially when working with mother machine experimental setups.
Not ideal if you're looking for a complete machine learning solution for image segmentation; this tool only generates the synthetic training data, not the segmentation model itself.
Stars
22
Forks
11
Language
Jupyter Notebook
License
GPL-2.0
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
Dependencies
19
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