Merge pull request #48 from raidionics/zenodo

README.md

@@ -17,6 +17,8 @@ app_file: demo/app.py
 [![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE)
 [![CI/CD](https://github.com/raidionics/AeroPath/actions/workflows/deploy.yml/badge.svg)](https://github.com/raidionics/AeroPath/actions/workflows/deploy.yml)
 <a target="_blank" href="https://huggingface.co/spaces/andreped/AeroPath"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a>
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10069288.svg)](https://doi.org/10.5281/zenodo.10069288)
+[![paper](https://img.shields.io/badge/arXiv-preprint-D12424)]([arXiv](https://arxiv.org/abs/2311.01138))
 
 **AeroPath** was developed by SINTEF Medical Image Analysis to accelerate medical AI research.
 
@@ -26,7 +28,7 @@ app_file: demo/app.py
 
 This repository contains the AeroPath dataset described in ["_AeroPath: An airway segmentation benchmark dataset with challenging pathology_"](https://arxiv.org/abs/2311.01138).  A web application was also developed in the study, to enable users to easily test our deep learning model on their own data. The application was developed using [Gradio](https://www.gradio.app) for the frontend and the segmentation is performed using the [Raidionics](https://raidionics.github.io/) backend.
 
-The dataset can be accessed [here](https://zenodo.org/records/10069289).
+The dataset is made openly available at Zenodo [here](https://zenodo.org/records/10069289).
 
 ## [Dataset structure](https://github.com/raidionics/AeroPath#data-structure)
 
@@ -104,6 +106,22 @@ If you found the dataset and/or web application relevant in your research, pleas
 }
 ```
 
+The dataset is hosted at Zenodo, so you should also cite the following:
+```
+@dataset{hofstad_2023_10069289,
+  author       = {Hofstad, Erlend and
+                  Bouget, David and
+                  Pedersen, André},
+  title        = {{AeroPath: An airway segmentation benchmark dataset 
+                   with challenging pathology}},
+  month        = nov,
+  year         = 2023,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.10069289},
+  url          = {https://doi.org/10.5281/zenodo.10069289}
+}
+```
+
 The web application is using the [Raidionics]() backend, thus, also consider citing:
 ```
 @article{bouget2023raidionics,