CelluleAnalyse 🔬

A modular, containerized web platform for automated fluorescence microscopy image analysis.

📋 Overview

CelluleAnalyse is a production-ready web platform for automated analysis of fluorescence microscopy images (.nd2, .czi, .tiff). It provides a complete pipeline from image loading to statistical reporting, with a modular architecture that supports multiple detection methods — from classical thresholding to deep learning.

Feature	Description	Status
🔵 Nucleus detection	Automated detection from DAPI/DNA channel	✅ Stable
🔬 Cell classification	Interphase vs mitosis classification	✅ Stable
📊 Fluorescence measurement	Intensity quantification in mitotic spindles	✅ Stable
📈 Group comparison	Statistical comparison between two conditions	🔄 In development
🧠 ML/DL detection	Cellpose, StarDist integration	🔄 In development

🚀 Quick Start — For End Users (No coding required)

⚠️ Only Docker Desktop is required — no Python, no Node.js, no source code needed.

Step 1 — Install Docker Desktop

Download and install Docker Desktop for your OS.*

🆘 First time installing Docker?

🇬🇧 Installation Guide (English)

🇫🇷 Guide d'installation (Français)

Step 2 — Download the 3 launcher files

Download only these 3 files from the deploy/ folder:

File	For
`docker-compose.yml`	All OS
`lancer.bat`	Windows only
`lancer.sh`	Mac / Linux only

Put all 3 files in the same folder.

Step 3 — Organize your images

Your images must be organized in subfolders like this:

MyImagesFolder/
    WT/
        image1.nd2
        image2.nd2
    MAP6_KO/
        image1.nd2
        image2.nd2

The subfolder names can be anything — WT, KO, Control, Treatment, etc.

Step 4 — Launch the app

Windows:

Double-click lancer.bat

Enter the path to your images folder when prompted (e.g. E:\MyLab\Images).

Mac / Linux:

chmod +x lancer.sh
./lancer.sh

Enter the path to your images folder when prompted (e.g. /Users/marie/Images).

The app will:

Download the Docker images automatically (~500MB, first launch only)
Start the application
Open http://localhost in your browser

Step 5 — Use the app

Click "Load WT folder" → enter the full path to your first group (e.g. E:\MyLab\Images\WT)
Click "Load KO folder" → enter the full path to your second group
Click on any file to visualize it
Navigate channels and Z-planes
Launch analysis from the Analysis panel

🐳 Docker Hub Images

The application is available as pre-built Docker images:

Image	Link
Backend (FastAPI)	`smill/celluleanalyse-backend:v0.2.0`
Frontend (React/Nginx)	`smill/celluleanalyse-frontend:v0.2.0`

Pull manually if needed:

docker pull smill/celluleanalyse-backend:v0.2.0
docker pull smill/celluleanalyse-frontend:v0.2.0

📸 Screenshots

Dashboard with image viewer, pipeline status, and analysis configuration

🏗️ Architecture

CelluleAnalyse/
├── backend/                          # FastAPI Python backend
│   ├── api/
│   │   ├── routes_chargement.py      # File loading routes
│   │   ├── routes_visualisation.py   # Image rendering routes
│   │   ├── routes_analyse.py         # Analysis routes
│   │   └── routes_rapport.py         # Report generation (coming soon)
│   ├── modules/
│   │   ├── chargement/
│   │   │   ├── loader_nd2.py         # Nikon .nd2 reader
│   │   │   ├── loader_czi.py         # Zeiss .czi reader (coming soon)
│   │   │   └── projection_z.py       # 3D to 2D Z-projection (max/mean/sum)
│   │   ├── detection_noyaux/
│   │   │   ├── base_detector.py      # Abstract detector interface
│   │   │   ├── detector_threshold.py # Otsu thresholding + watershed
│   │   │   ├── detector_cellpose.py  # Cellpose ML (coming soon)
│   │   │   └── detector_stardist.py  # StarDist DL (coming soon)
│   │   ├── classification/
│   │   │   ├── base_classifier.py    # Abstract classifier interface
│   │   │   ├── classifier_shape.py   # Geometric shape classifier
│   │   │   ├── classifier_ml.py      # ML classifier (coming soon)
│   │   │   └── classifier_dl.py      # DL classifier (coming soon)
│   │   ├── detection_fuseau/
│   │   │   ├── base_fuseau.py        # Abstract spindle detector
│   │   │   ├── fuseau_threshold.py   # Threshold-based detection
│   │   │   └── fuseau_ml.py          # ML-based detection (coming soon)
│   │   ├── intensite/
│   │   │   └── mesure_intensite.py   # Fluorescence intensity measurement
│   │   └── statistiques/
│   │       ├── stats_comparaison.py  # WT vs KO statistical comparison
│   │       └── rapport_generator.py  # HTML/PDF report generation
│   ├── cache_shared.py               # Shared in-memory cache
│   ├── main.py                       # FastAPI app entry point
│   └── requirements.txt
│
├── frontend/                         # React frontend
│   └── src/
│       ├── components/
│       │   ├── layout/               # Header, Sidebar, MainContent
│       │   ├── groups/               # Group loader and file list
│       │   ├── pipeline/             # Pipeline status tracker
│       │   ├── viewer/               # Image viewer, channel selector, Z-slider
│       │   ├── metrics/              # Metric cards and grid
│       │   ├── files/                # File list component
│       │   └── analysis/             # Method selector and config panel
│       ├── api/                      # Backend API calls
│       ├── context/                  # Global app state (AppContext)
│       └── styles/                   # CSS variables (light/dark theme)
│
├── deploy/                           # End-user distribution files
│   ├── v0.1.0/                       # Version 0.1.0
│   │   ├── docker-compose.yml
│   │   ├── lancer.bat
│   │   └── lancer.sh
│   └── v0.2.0/                       # Version 0.2.0 (latest)
│       ├── docker-compose.yml
│       ├── lancer.bat
│       └── lancer.sh
│
├── Dockerfile.backend
├── Dockerfile.frontend
├── docker-compose.yml                # Development docker-compose
├── nginx.conf
└── README.md

🔬 Supported Image Formats

Format	Manufacturer	Status
`.nd2`	Nikon	✅ Supported
`.czi`	Zeiss	🔄 Coming soon
`.tiff`	Universal	🔄 Coming soon
`.lif`	Leica	🔄 Coming soon
`.oib`	Olympus	🔄 Coming soon

🧠 Detection Methods

Module	Method	Status
Nucleus detection	Otsu thresholding + Watershed	✅ Available
Nucleus detection	Cellpose (ML)	🔄 Coming soon
Nucleus detection	StarDist (DL)	🔄 Coming soon
Nucleus detection	Custom model (YOLO/fine-tuned)	🔄 Coming soon
Classification	Geometric shape (circularity)	✅ Available
Classification	Classical ML	🔄 Coming soon
Classification	Deep Learning	🔄 Coming soon
Spindle detection	Threshold-based	🔄 Coming soon
Spindle detection	ML-based	🔄 Coming soon

💻 Local Development

Backend

cd backend
pip install -r requirements.txt
python -m uvicorn main:app --reload
# http://localhost:8000
# http://localhost:8000/docs (Swagger UI)

Frontend

cd frontend
npm install
npm run dev
# http://localhost:5173

Build Docker images locally

docker-compose build
docker-compose up -d

🛠️ Technical Stack

Category	Technology	Version
Backend framework	FastAPI	0.128
ASGI server	Uvicorn	0.39
Image reading	nd2	0.11
Image processing	scikit-image	0.21
Numerical computing	NumPy	1.26
Data analysis	Pandas	2.0
Visualization	Matplotlib, Seaborn	3.7, 0.12
Frontend framework	React	18
Build tool	Vite	8
HTTP client	Axios	1.4
Web server	Nginx	1.27
Containerization	Docker + Compose	28.x
Languages	Python, JavaScript, CSS	3.11, ES2022

📊 Use Case

This platform was developed for the analysis of fluorescence microscopy images comparing:

WT cells (wild-type) — normal cells
MAP6 KO cells — cells depleted of the MAP6 microtubule-associated protein

Research question: Does MAP6 depletion affect tubulin modifications (acetylation, polyglutamylation) in mitotic spindles?

Pipeline:

Load .nd2 files from both conditions
Detect nuclei from DNA/DAPI channel (Z-max projection)
Classify cells: interphase (round nucleus) vs mitosis (irregular shape)
Measure fluorescence intensity in channels 1 & 2 within mitotic spindles
Compare WT vs MAP6 KO — statistical report

📚 Documentation

📄 Citation

@software{celluleanalyse2026,
  author = {Millimono, Sory ; Paim , Lia Gomes},
  title  = {CelluleAnalyse: A modular web platform for automated fluorescence microscopy analysis},
  year   = {2026},
  url    = {https://github.com/Millimono/CelluleAnalyse}
}

👤 Author

Sory Millimono — Bioinformatician · AI Researcher Université de Montréal

Lia Gomes Paim — Professeure adjointe Faculté de médecine vétérinaire - Département de biomédecine HY 3200Sicotte 2980

📧 millimono64.sm@gmail.com 🔗 LinkedIn 🔬 ORCID

📜 License

MIT License — see LICENSE for details.

Navigation Menu

Repository files navigation

CelluleAnalyse 🔬

📋 Overview

🚀 Quick Start — For End Users (No coding required)

Step 1 — Install Docker Desktop

Step 2 — Download the 3 launcher files

Step 3 — Organize your images

Step 4 — Launch the app

Step 5 — Use the app

🐳 Docker Hub Images

📸 Screenshots

🏗️ Architecture

🔬 Supported Image Formats

🧠 Detection Methods

💻 Local Development

Backend

Frontend

Build Docker images locally

🛠️ Technical Stack

📊 Use Case

📚 Documentation

📄 Citation

👤 Author

📜 License

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Name		Name	Last commit message	Last commit date
backend		backend
deploy		deploy
docs/images		docs/images
frontend		frontend
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile.backend		Dockerfile.backend
Dockerfile.frontend		Dockerfile.frontend
LICENSE		LICENSE
README.md		README.md
docker-compose.yml		docker-compose.yml
git		git
lancer.bat		lancer.bat
lancer.sh		lancer.sh
main		main
nginx.conf		nginx.conf

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CelluleAnalyse 🔬

📋 Overview

🚀 Quick Start — For End Users (No coding required)

Step 1 — Install Docker Desktop

Step 2 — Download the 3 launcher files

Step 3 — Organize your images

Step 4 — Launch the app

Step 5 — Use the app

🐳 Docker Hub Images

📸 Screenshots

🏗️ Architecture

🔬 Supported Image Formats

🧠 Detection Methods

💻 Local Development

Backend

Frontend

Build Docker images locally

🛠️ Technical Stack

📊 Use Case

📚 Documentation

📄 Citation

👤 Author

📜 License

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