Heart dynamics conditioned on brain

Code related to the paper:

Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

by Banus, Jaume and Sermesant, Maxime and Camara, Oscar and Lorenzi, Marco.

Link:

BibTeX citation:

@article{Banus2020,
author = {Banus, Jaume and Sermesant, Maxime and Camara, Oscar and Lorenzi, Marco},
booktitle = {MICCAI},
title = {Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets. Medical Image Computing and Computer Assisted Intervention},
year = {2020}
}

Installation

1. Create/activate a virtual environment

• Install conda:

GNU / Linux

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  
bash Miniconda3-latest-Linux-x86_64.sh

Download this github repository and move into in:

git clone https://gitlab.inria.fr/epione/hdcob
cd hdcob

Windows

Download and install conda from: https://docs.conda.io/en/latest/miniconda.html Download this github repository from: https://gitlab.inria.fr/epione/hdcob Open the Anaconda prompt and move into the github repository previously downloaded.

2. Dependencies

• Install the customized python environment:

conda env create -f environment.yml

Activate the python environment:

conda activate py37

• Or install the dependencies listed in 'requirements.txt'

3. Package installation, when we are inside the folder "hdcob"

• Using pip

pip install -e .

• Manually

Install the hdcob package:

python setup.py install

An alternative to the last point is to install the package in "develop" mode. Using this mode, all local modifications of source code will be considered in your Python interpreter (when restarted) without having to install the package again. This is particularly useful when adding new features. To install this package in develop mode, type the following command line:

python setup.py develop

Examples

In the folder hdcob/example you will find a code training our model VIRCA (Variational imputation and regression framework based on conditional autoencoding) and comparing it to classical imputation approaches in synthetic data.