An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

This repository reproduces the results published in the paper An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids [1]

Specifically, it especially implements three tasks:

• Organoid segmentation
• Global cysticity classification
• Local cyst segmentation

For the implementation of the 3D U-Net, the full credit goes to Adrian Wolny (https://github.com/wolny/pytorch-3dunet).

Prerequisites

• Operating system: Windows or Linux (tested on Ubuntu 20.04)
• Install Anaconda

Installation

git clone https://github.com/deiluca/cerebral_organoid_quant_mri

Install conda environment

cd path/to/cerebral_organoid_quant_mri
conda env create -f environment.yml

Activate the conda environment:

conda activate co_quant_mri

Add this line to ~/.bashrc to permanently add the repository to PYTHONPATH

export PYTHONPATH="${PYTHONPATH}:path/to/cerebral_organoid_quant_mri"

Data preparation

1. Download the data from Zenodo and unpack it in data/data_zenodo.

2. Image extraction and data preparation

   python scripts/extract_and_prepare_images.py
   

Organoid segmentation

1. Train and test 3D U-Net. (can be skipped: checkpoints from previous run are located here)

   python scripts/train.py org_seg
   python scripts/test.py org_seg
   

2. Extract and inspect results using scripts/data_analysis.ipynb

Model performance (Test Dice 0.92±0.06 [mean±SD])

Example of segmentation performance (org7_0530)

Global cysticity classification

See scripts/data_analysis.ipynb

Performance of Compactness and examples of low- and high-quality organoids

DW-MRI: Higher diffusion of low-quality organoids

Local cyst segmentation

1. Train and test 3D U-Net. (can be skipped: checkpoints from previous run are located here)

   python scripts/train.py local_cyst_seg
   python scripts/test.py local_cyst_seg
   

2. Extract and inspect results using scripts/data_analysis.ipynb

Model performance (Test Dice 0.63±0.15 [mean±SD])

Example of segmentation performance (org7_0530)

Is compactness a predictor of organoid cysticity?

Yes, high correlation. Extract and inspect results using scripts/data_analysis.ipynb

Please note that repeated 3D U-Net training runs might lead to slightly different results. This is caused by random initialization of 3D U-Net weights.

Cite

If you find this useful, please consider citing our work:

[1] Deininger, L., Jung-Klawitter, S., Mikut, R. et al. An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids. Sci Rep 13, 21231 (2023).

@article{Deininger2023,
  title = {An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids},
  volume = {13},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-023-48343-7},
  DOI = {10.1038/s41598-023-48343-7},
  number = {1},
  journal = {Scientific Reports},
  publisher = {Springer Science and Business Media LLC},
  author = {Deininger,  Luca and Jung-Klawitter,  Sabine and Mikut,  Ralf and Richter,  Petra and Fischer,  Manuel and Karimian-Jazi,  Kianush and Breckwoldt,  Michael O. and Bendszus,  Martin and Heiland,  Sabine and Kleesiek,  Jens and Opladen,  Thomas and H\"{u}bschmann,  Oya Kuseyri and H\"{u}bschmann,  Daniel and Schwarz,  Daniel},
  year = {2023},
  month = dec 
}