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trustworthy-ai-fetal-brain-segmentation

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Trustworthy AI method based on Dempster-Shafer theory - application to fetal brain 3D T2w MRI segmentation

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# A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Trustworthy AI method based on Dempster-Shafer theory with application to fetal brain 3D T2w MRI segmentation.
![auto-seg](https://user-images.githubusercontent.com/17875992/174453165-2ab9c26b-14da-4728-bec3-710166d12f7b.gif)

## System requirements
### Hardware requirements
To run the automatic segmentation algorithms a NVIDIA GPU with at least 8GB of memory is required.

The code has been tested with the configuration:
* 12 Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz
* 1 NVIDIA GPU GeForce GTX 1070 with 8GB of memory

### Operating system requirements
The code is supported on every operating system (OS) using docker.
However, it has been tested only for
* Linux Ubuntu 18.04.6 LTS
* Linux Ubuntu 20.04.3 LTS

## Installation
The installation is performed using docker:

First, install docker (see https://docs.docker.com/get-docker/).

### Installation of the docker image
Install the docker image  ```twai:latest``` using
```bash
sh build_docker.sh
```
This step takes a few minutes.

### Create and start a docker container
Create a docker container for the docker image
 ```twai:latest``` that was previously built, using the command
 ```bash
docker run --ipc=host --gpus all -it -v <repository-path>:/workspace/trustworthy-ai-fetal-brain-segmentation -v <data-path>:/data --name twai twai:latest
```
where ```<repository-path>``` has to be replaced by the path of the git repository on your system
and ```<data-path>``` has to be replaced by the path of a folder containing the data to be used for segmentation.
This step creates a docker container called ```twai```.

If you have already created the docker container ```twai```, you can reuse it using the command lines
```bash
docker start twai
docker attach twai
```

The installation has been tested for
* Docker version 20.10.12, build e91ed57

## How to use

### Automatic Fetal Brain 3D MRI Segmentation
You can compute the automatic segmentations for the backbone AI, fallback, and trustworthy AI algorithms
 using the python script ```run_segment.py```.

To learn more about the usage of the script, please see
```bash
python run_segment.py -h
```
 
We refer to the demo below for a detailed example.

### Demonstration: example case
Fetal brain 3D MRI from a subset of the testing dataset can be downloaded at
https://zenodo.org/record/6405632#.YkbWPCTMI5k

Put the folder ```\sub-feta001``` of the first case in ```<data-path>``` on your system.

Start and attach the docker container (see above).

You can now compute the automatic segmentations for the backbone AI, fallback, and trustworthy AI algorithms using,
 inside the docker container
```bash
python run_segment.py --input '/data/sub-feta001/srr.nii.gz' --mask '/data/sub-feta001/mask.nii.gz' --ga 27.9 --condition 'Spina Bifida' --output_folder 'output/sub-feta001' --bfc
```
This step takes several minutes.
You may need to adapt the paths depending where the folder ```\sub-feta001``` is located inside ```<data-path>```.

For more information about the argument of ```run_segment.py``` please run
```bash
python run_segment.py -h
```
The output can be found in the folder pointed by ```--output_folder``` (```output/sub-feta001'``` in the example above).
The output folder contains three main folders of interest:
 * ```\backboneAI```: output segmentation of the deep learning method [nnU-Net][nnunet]
 * ```\fallback```: output segmentation of the atlas-based method
 * ```\trustworthyAI```: output segmentation of our trustworthy AI algorithm combining the output of the backbone AI and the fallback algorithms
 
Each of those folders should contain a unique segmentation file with the extension ```.nii.gz``` corresponding to the
segmentation computed by the algorithm of the same name as the folder name.

### Generating figures
The figures shown in the paper can be reproduced by running
```bash
sh run_make_all_figures.sh
```
After running this command, the figures will be in the folder ```\output```.

## How to cite
If you find this research useful for your work, please give this repo a star :star: and cite
* L. Fidon, M. Aertsen, F. Kofler, A. Bink, A. L. David, T. Deprest, D. Emam, F. Guffens, A. Jakab, G. Kasprian,
 P. Kienast, A. Melbourne, B. Menze, N. Mufti, I. Pogledic, D. Prayer, M. Stuempflen, E. Van Elslander, S. Ourselin, 
 J. Deprest, T. Vercauteren.
 [A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation][twai]

```
@article{fidon2022dempster,
  title={A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation},
  author={Fidon, Lucas and Aertsen, Michael and Kofler, Florian and Bink, Andrea and David, Anna L and Deprest, Thomas and Emam, Doaa and Guffens, Fr and Jakab, Andr{\'a}s and Kasprian, Gregor and others},
  journal={arXiv preprint arXiv:2204.02779},
  year={2022}
}
```

[twai]: https://arxiv.org/abs/2204.02779
[nnunet]: https://github.com/MIC-DKFZ/nnUNet

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