README.md

metadata

title: Cellpose + SAM DRG Segmentation
license: mit
sdk: streamlit
emoji: 🏃
colorFrom: indigo
colorTo: purple
app_file: streamlit_app.py

Spinal Cord Segmentation Pipeline

Automated, end‑to‑end processing and segmentation of spinal‑cord microscopy images with Cellpose.

Overview

This repository provides a turn‑key workflow for turning raw histological slides of the spinal cord (TIFF) into high‑quality, full‑resolution segmentation masks—in a single command.

Key Features

Stage	Purpose
TIFF → PNG conversion	Converts raw .tiff slides to compressed .png, with optional down‑scaling to speed up processing.
Smart tiling	Splits very large images into manageable tiles that fit comfortably in GPU/CPU memory.
Cellpose inference	Runs the cyto3 (default) or any other Cellpose model on every tile.
Mask stitching	Re‑assembles the individual tile masks into a single, full‑resolution segmentation mask.

Requirements

• Python 3.9+
• GPU‑enabled PyTorch build (optional but recommended)
• Dependencies (installed automatically via requirements.txt):
  • cellpose==3.1.1.1
  • opencv‑python
  • numpy
  • pillow
  • tifffile

Installation

# Clone the repository
git clone https://github.com/your‑username/spinal‑cord‑segmentation.git
cd spinal‑cord‑segmentation

# Create / activate a virtualenv (optional but recommended)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install Python dependencies
pip install -r requirements.txt

Quick Start

1. Place your raw .tiff images in data/input/ (or adjust the paths in bin/constants.py).

2. Run the pipeline:

   python main.py
   

3. Collect your results:

   • PNG conversions → data/png/
   • Split tiles → data/tiles/
   • Cellpose masks → data/masks/
   • Stitched masks → data/output/

Detailed Workflow

flowchart LR
    A[TIFF images] --> B[generate_pngs.py]:::step
    classDef step fill:#fafafa,stroke:#333,stroke-width:1px;
    B --> C[generate_split_images.py]:::step
    C --> D[run_cellpose.py]:::step
    D --> E[generate_masks.py]:::step
    E --> F[Final segmentation]:::step

All paths, tile overlap, and Cellpose parameters are configurable in bin/constants.py.

Project Layout

.
├── main.py              # Orchestrates the full pipeline
├── bin/
│   ├── constants.py     # Centralised paths & tunables
│   ├── generate_pngs.py # TIFF → PNG converter
│   ├── generate_split_images.py
│   └── generate_masks.py
├── model/
│   └── run_cellpose.py  # Wrapper around Cellpose API
├── requirements.txt
└── LICENSE

License

Distributed under the terms of the MIT License. See LICENSE for full text.

Contributing

Contributions, issues and feature requests are welcome! Please open an issue or submit a pull request — and ensure your code passes flake8/black checks and includes appropriate tests.

Citation

If you use this pipeline in your research, please cite Cellpose and this repository:

@article{stringer_cellpose_2021,
  title   = {Cellpose: a generalist algorithm for cellular segmentation},
  author  = {Stringer, Carsen and Pachitariu, Marius},
  journal = {Nature Methods},
  year    = {2021}
}