An image analysis pipeline to quantify the spatial distribution of cell markers in stroma-rich tumors

Overview

This repository contains the code for the image analysis pipeline used to quantify the spatial distribution of cell markers in stroma-rich tumors.

Features

• Segmentation of nuclei in fluorescent images using pre-trained StarDist 2D
• Machine-learning based composite classification of cancer cells of interest
• Statistical propagation of classification thresholds across multiple images
• Spatial distribution using cell-stroma 2D signed distance
• Compatible with high-resolution whole-slide images
• Visualization, sensitivity and statistical analysis of results in Python

Install dependencies

git clone https://github.com/HMS-IAC/stroma-spatial-analysis-web.git
cd stroma-spatial-analysis-web

# use uv to install dependencies
uv venv
uv pip install -r requirements.txt

Datasets

Raw images can be downloaded from https://zenodo.org/records/15297453.

Distribution

Our image analysis pipeline is distributed as a collection of Groovy scripts for image processing in QuPath and Python Jupyter notebooks for statistical analysis and visualization.

Running the pipeline on your own images

To run the pipeline on your own data, follow the steps below:

1. Load your images into QuPath and create a project.

2. Run the scripts in the qupath_scripts folder in the following order:

   1. stardist_cell_detection.groovy (this step requires the StarDist extension for QuPath – the installation tutorial can be found here)
   2. cell_classification.groovy if using threshold-based classification, otherwise follow these instructions from QuPath to train a machine learning classifier .
   3. stroma_annotator.groovy (make sure to adapt the thresholds to your images)
   4. Export measurements as a CSV file using QuPath's export feature, or using the export_cell_measurements.groovy script.
   5. stroma_annotator_sensitivity.groovy to run sensitivity analysis on the stroma annotation.

3. Run the Jupyter notebooks cell-by-cell in the analysis folder to analyze the exported measurement tables. Make sure to adapt the paths to your data and results.

License

This project is licensed under the MIT License. See the LICENSE file for details.