/pre_post_synthesis

Official repository for "Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation"

Primary LanguagePython

Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation

In SPIE Medical Imaging 2024.

examples

Getting Started

The Duke Dataset used in this study is available on The Cancer Imaging Archive (TCIA).

You may find some examples of synthetic nifti files in synthesis/examples.

Synthesis Code

  • Config to run a training of the image synthesis model.
  • Config to run a test of the image synthesis model.
  • Code to transform Duke DICOM files to NiFti files.
  • Code to extract 2D pngs from 3D NiFti files.
  • Code to create 3D NiFti files from axial 2D pngs.
  • Code to separate synthesis training and test cases.
  • Code to compute the image quality metrics such as SSIM, MSE, LPIPS, and more.
  • Code to compute the Frèchet Inception Distance (FID) based on ImageNet and RadImageNet.

Segmentation Code

  • Code to prepare 3D single breast cases for nnunet segmentation.
  • Train-test-splits of the segmentation dataset.
  • Script to run the full nnunet pipeline on the Duke dataset.

Run the model

Model weights are stored on on Zenodo and made available via the medigan library.

To create your own post-contrast data, simply run:

pip install medigan
# import medigan and initialize Generators
from medigan import Generators
generators = Generators()

# generate 10 samples with model 23 (00023_PIX2PIXHD_BREAST_DCEMRI). 
# Also, auto-install required model dependencies.
generators.generate(model_id='00023_PIX2PIXHD_BREAST_DCEMRI', num_samples=10, install_dependencies=True)

Reference

Please consider citing our work if you found it useful for your research:

@article{osuala2023pre,
  title={{Pre-to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation}},
  author={Osuala, Richard and Joshi, Smriti and Tsirikoglou, Apostolia and Garrucho, Lidia and Pinaya, Walter HL and Diaz, Oliver and Lekadir, Karim},
  journal={arXiv preprint arXiv:2311.10879},
  year={2023}
  }

Acknowledgements

This repository borrows code from the pix2pixHD and the nnUNet repositories. The 254 tumour segmentation masks used in this study were provided by Caballo et al.