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--- |
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title: Cellpose + SAM DRG Segmentation |
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license: mit |
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sdk: streamlit |
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emoji: 🏃 |
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colorFrom: indigo |
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colorTo: purple |
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app_file: streamlit_app.py |
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--- |
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# Spinal Cord Segmentation Pipeline |
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Automated, end‑to‑end processing and segmentation of spinal‑cord microscopy images with [Cellpose](https://cellpose.readthedocs.io/). |
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## Overview |
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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*. |
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## Key Features |
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| Stage | Purpose | |
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|-------|---------| |
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| **TIFF → PNG conversion** | Converts raw `.tiff` slides to compressed `.png`, with optional down‑scaling to speed up processing. | |
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| **Smart tiling** | Splits very large images into manageable tiles that fit comfortably in GPU/CPU memory. | |
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| **Cellpose inference** | Runs the *cyto3* (default) or any other Cellpose model on every tile. | |
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| **Mask stitching** | Re‑assembles the individual tile masks into a single, full‑resolution segmentation mask. | |
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## Requirements |
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* Python **3.9+** |
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* GPU‑enabled PyTorch build (optional but recommended) |
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* Dependencies (installed automatically via `requirements.txt`): |
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* `cellpose==3.1.1.1` |
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* `opencv‑python` |
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* `numpy` |
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* `pillow` |
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* `tifffile` |
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## Installation |
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```bash |
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# Clone the repository |
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git clone https://github.com/your‑username/spinal‑cord‑segmentation.git |
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cd spinal‑cord‑segmentation |
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# Create / activate a virtualenv (optional but recommended) |
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python -m venv .venv |
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source .venv/bin/activate # Windows: .venv\Scripts\activate |
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# Install Python dependencies |
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pip install -r requirements.txt |
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``` |
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## Quick Start |
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1. **Place** your raw `.tiff` images in `data/input/` (or adjust the paths in `bin/constants.py`). |
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2. **Run** the pipeline: |
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```bash |
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python main.py |
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``` |
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3. **Collect** your results: |
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* PNG conversions → `data/png/` |
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* Split tiles → `data/tiles/` |
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* Cellpose masks → `data/masks/` |
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* Stitched masks → `data/output/` |
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## Detailed Workflow |
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```mermaid |
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flowchart LR |
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A[TIFF images] --> B[generate_pngs.py]:::step |
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classDef step fill:#fafafa,stroke:#333,stroke-width:1px; |
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B --> C[generate_split_images.py]:::step |
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C --> D[run_cellpose.py]:::step |
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D --> E[generate_masks.py]:::step |
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E --> F[Final segmentation]:::step |
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``` |
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*All paths, tile overlap, and Cellpose parameters are configurable in* **`bin/constants.py`**. |
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## Project Layout |
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``` |
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. |
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├── main.py # Orchestrates the full pipeline |
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├── bin/ |
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│ ├── constants.py # Centralised paths & tunables |
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│ ├── generate_pngs.py # TIFF → PNG converter |
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│ ├── generate_split_images.py |
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│ └── generate_masks.py |
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├── model/ |
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│ └── run_cellpose.py # Wrapper around Cellpose API |
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├── requirements.txt |
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└── LICENSE |
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``` |
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## License |
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Distributed under the terms of the **MIT License**. See `LICENSE` for full text. |
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## Contributing |
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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. |
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## Citation |
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If you use this pipeline in your research, please cite *Cellpose* **and** this repository: |
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```text |
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@article{stringer_cellpose_2021, |
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title = {Cellpose: a generalist algorithm for cellular segmentation}, |
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author = {Stringer, Carsen and Pachitariu, Marius}, |
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journal = {Nature Methods}, |
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year = {2021} |
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} |
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``` |
