Automated, end‑to‑end processing and segmentation of spinal‑cord microscopy images with Cellpose.
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.
| 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. |
- Python 3.9+
- GPU‑enabled PyTorch build (optional but recommended)
- Dependencies (installed automatically via
requirements.txt):cellpose==3.1.1.1opencv‑pythonnumpypillowtifffile
# 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-
Place your raw
.tiffimages indata/input/(or adjust the paths inbin/constants.py). -
Run the pipeline:
python main.py
-
Collect your results:
- PNG conversions →
data/png/ - Split tiles →
data/tiles/ - Cellpose masks →
data/masks/ - Stitched masks →
data/output/
- PNG conversions →
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.
.
├── 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
Distributed under the terms of the MIT License. See LICENSE for full text.
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.
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}
}