THIS REPO WAS ARCHIVED 02/06/2026. VISIT https://github.com/biosustain/mycol TO ACCESS THE MAINTAINED VERSION OF MYCOL.

Mycol

A lightweight, human-in-the-loop microscopy image analysis app.

Mycol is a Streamlit-based application that makes machine-learning-assisted microscopy analysis accessible to non-specialists. It enables fast annotation, automated segmentation and classification, model fine-tuning, and quantitative phenotyping, all on a standard laptop and without coding.

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Features

Annotation & QC

• Upload images and optional masks
• Manual mask drawing and editing
• SAM2-guided segmentation
• Automated Cellpose segmentation (single or batch mode)
• Interactive classification (manual or DenseNet-based)

Model Fine-Tuning

• Train Cellpose (segmentation) and DenseNet (classification) models directly in the app
• Default training settings for general use
• Diagnostic outputs:
  • Loss curves
  • IoU scores
  • True vs. predicted counts
  • Accuracy, precision, F1, confusion matrix
• Download trained models and training summaries

Cell Metrics & Phenotyping

• Automatic computation of cell descriptors (size, shape, elongation, compactness, etc.)
• Visual comparison of phenotypic classes
• Export plots and tabulated descriptors
• Built-in explanations for descriptor interpretation

Lightweight & Accessible

• Runs locally on standard hardware
• Minimal dependencies
• Designed for small-scale workflows

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Installation

Note

This project uses uv as its package manager. It is a drop-in replacement for pip and conda that handles the virtual environment and dependencies for you. To install it, run pip install uv or follow the official instructions.

1. Clone the repository

git clone https://github.com/sambra95/mycol.git

2. Navigate into the repository in your terminal

cd mycol

3. Install dependencies

This automatically creates a virtual environment and installs everything the app needs.

uv sync

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Run the App locally

From inside the repository, run:

uv run streamlit run app.py

uv run executes the command inside the project's virtual environment. Alternatively, you can activate the environment first (source .venv/bin/activate on macOS/Linux or .venv\Scripts\activate on Windows) and then run streamlit run app.py.

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Example Use Cases

• Rapid cell counting
• Creating curated datasets of annotated images
• Automating image annotation (with human QC)
• Morphology-based phenotypic comparison

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License

MIT