Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation

This repository is the code for Tempera submitted to the M&Ms-2 challenge: https://www.ub.edu/mnms-2/. Details of the model can be found at: https://link.springer.com/chapter/10.1007/978-3-030-93722-5_29

Setup

The code is implemented in Python and all libraries and their versions can be found in the file 'environment.yml'.

Data

The data is publicly available and can be obtained from: https://www.ub.edu/mnms-2/. The model expects the data to be located at:

mnms2_challenge/data/trainining
mnms2_challenge/data/validation

where training contains the samples from 1-160 and validation the samples from 161-200.

Training the model

To train the model, simply run:

python src/run_training.py

Inference

To make predictions using the trained model, first copy the trained weights of the model to:

src/model_weights/multi_stage_model/model.weights.h5

and run the inferenve script by:

python src/run_inference.py <input_path> <output_path>

Citation

If you found this code useful for your project please cite as:

@inproceedings{galazis2021tempera,
  title={Tempera: Spatial transformer feature pyramid network for cardiac MRI segmentation},
  author={Galazis, Christoforos and Wu, Huiyi and Li, Zhuoyu and Petri, Camille and Bharath, Anil A and Varela, Marta},
  booktitle={International Workshop on Statistical Atlases and Computational Models of the Heart},
  pages={268--276},
  year={2021},
  organization={Springer}
}

Acknowledgement

This project was supported by the UK Research and Innovation (UKRI) Centres of Doctoral Training (CDT) in Artificial Intelligence for Healthcare (AI4H) http://ai4health.io (Grant No. EP/S023283/1) and the British Heart Foundation Centre of Research Excellence at Imperial College London (RE/18/4/34215).