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, QuPath projects and measurement tables 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. The notebooks are also distributed as Colab notebooks--see links below.

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 template folder to analyze the exported measurement tables. Make sure to adapt the paths to your data and results.

   1. Exploratory analysis:
   2. Statistical mapping of thresholds:
   3. Confusion matrix:
   4. Spatial analysis:
   5. Sensitivity analysis:

License

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

Citation

Please cite as:

Ruzette AA, Kozlova N, Cruz KA, Muranen T, Nørrelykke SF. An image analysis pipeline to quantify the spatial distribution of cell markers in stroma-rich tumors. bioRxiv 2025.04.28.650414; doi: https://doi.org/10.1101/2025.04.28.650414