This repository provides a PyTorch implementation of the DeepNORAH method presented in our MICCAI 2023 paper ”Learning normal asymmetry representations for homologous brain structures”.
You find a PDF of the Learning normal asymmetry representations for homologous brain structures MICCAI 2023 paper at https://ignaciorlando.github.io/publication/2023-miccai/.
If you use our work, please also cite the paper:
@inproceedings{deangeli2023learning,
title={Learning normal asymmetry representations for homologous brain structures},
author={Deangeli, Duilio and Iarussi, Emmanuel and Princich, Juan Pablo and Bendersky, Mariana and Larrabide, Ignacio and Orlando, José Ignacio},
booktitle={26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
year={2023},
publisher={Springer}
}
If you would like to get in touch, please contact ddeangeli@pladema.exa.unicen.edu.ar.
Although normal homologous brain structures are approximately symmetrical by definition, they also have shape differences due to e.g. natural ageing. On the other hand, neurodegenerative conditions induce their own changes in this asymmetry, making them more pronounced or altering their location. Identifying when these alterations are due to a pathological deterioration is still challenging. Current clinical tools rely either on subjective evaluations, basic volume measurements or disease-specific deep learning models. This paper introduces a novel method to learn normal asymmetry patterns in homologous brain structures based on anomaly detection and representation learning. Our framework uses a Siamese architecture to map 3D segmentations of left and right hemispherical sides of a brain structure to a normal asymmetry embedding space, learned using a support vector data description objective. Being trained using healthy samples only, it can quantify deviations-from-normal-asymmetry patterns in unseen samples by measuring the distance of their embeddings to the center of the learned normal space. We demonstrate in public and in-house sets that our method can accurately characterize normal asymmetries and detect pathological alterations due to Alzheimer’s disease and hippocampal sclerosis, even though no diseased cases were accessed for training.
This code is written in Python 3.7 and requires the packages listed in requirements.txt.
Clone the repository to your local machine and directory of choice:
git clone https://github.com/duiliod/DeepNORHA.git
To run the code, we recommend setting up a virtual environment, e.g. using virtualenv or conda:
# pip install virtualenv
cd <path-to-DeepNORAH-directory>
virtualenv myenv
source myenv/bin/activate
pip install -r requirements.txt
cd <path-to-DeepNORAH-directory>
conda create --name myenv
source activate myenv
while read requirement; do conda install -n myenv --yes $requirement; done < requirements.txt
cd <path-to-DeepNORAH-directory>
# activate virtual environment
source myenv/bin/activate # or 'source activate myenv' for conda
# change to source directory
cd src/trainer
# run experiment
python train_SVDD.py --configAE '../../configs/AE_LeNet_bothHippocampiFlip_lr_001_rd_32_MSE_16filters.yml' --configSVDD '../../configs/AE_LeNet_bothHippocampiFlip_lr_001_rd_32_MSE_16filtersSVDD.yml'
This example trains a DeepNORAH model where healthy hippocampi is considered to be the normal class.
MIT