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Project 11: 3D Matrix Motility Map (3DM³)

A collaboration with the Institute of Cell Biology and Immunology, University of Stuttgart, Germany

🧠 Introduction

Cancer cell dissemination in 3D matrices is driven by interactions between cells and the extracellular matrix (ECM), especially collagen.
This project focuses on live-cell imaging of breast tumor spheroids embedded in collagen to analyze cancer cell migration, shape, and interaction with ECM architecture.

The dataset includes 5D live imaging (X, Y, Z, T, C) acquired using widefield and confocal microscopy.
Channels include:

  • Brightfield (cell shape)
  • mScarlet (nuclear marker)
  • Second Harmonic Generation (SHG) (collagen structure)

Our aim is to develop a reproducible pipeline for:

  • Stitching tiled acquisitions
  • Denoising (via N2V or Careamics)
  • Manual annotation
  • Cellpose finetuning
  • Segmentation of migrating cells

🔧 Pipeline Overview

1. Environment Setup

To enable GPU acceleration on Windows:

conda env create -f n2v_env.yaml
conda activate n2v

For denoising, choose either:

  • Noise2Void (legacy, self-supervised)
  • ✅ Careamics (recommended, modular and modern implementation)

2. Stitching Raw Tile Images

We begin with a 2x2 tile grid of 5D TIFF files (shape: X, Y, Z, T, C). Stitching is done using known positional offsets.

Run:

python stitch.py

Output: Single .ome.tif containing the full volume.

3. Preparing 2D+T Slices for Denoising

We extract the nuclei channel (mScarlet), and apply max Z-projection over central slices to obtain a 3D (T, Y, X) stack.

Run:

python prepare_n2v.py

4. Denoising

You can denoise using:

Option A: Jupyter Notebook with N2V

Open:

notebooks/N2V_denoising.ipynb

Run the cells, specify where your dataset lies and denoise. Remember to install the environment.

Option B: Jupyter Notebook with CAREamics (Recommended)

Open:

CAREamics_denoising.ipynb

Again, run each cell and specify the dataset. This will need a different environment. For any assistance, consult CAREamics' documentation.

Here you can see an image representing (from top to bottom) the original image, the image after being properly stitched and preprocessed and after denoising with CAREamics.

Original Image - Preprocessed - Denoised

5. Cell Annotation and Training

5.1 Manual Annotation

Use the Cellpose GUI for annotation:

python -m cellpose --Zstack

Save training pairs like:

segmentation_input/training/
├── nuclei_t003.tif
├── nuclei_t003_seg.npy

To have a consisten training of cellpose you will need to annotate between 15-20 nuclei per time-point and multiple time-points per scene. If not, there is not enough data to fine-tune cellpose.

For more information on how to annotate images read this docs and this blog post.

5.2 Training Custom Cellpose Model

We use a batch file to launch training from these annotation pairs.

File to run:

scripts/train_cellpose.bat

This runs Cellpose training with:

  • base model: nuclei
  • 50 epochs
  • batch size 8
  • grayscale channel (chan=0)

Trained model is saved to:

models/cellpose_nuclei_model/nuclei_custom/

Acknowledgements

AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.