Update README.md

README.md

@@ -1,9 +1,12 @@
 ---
-license: mit
+title: AscitesModel
+license: cc-by-nc-sa-4.0
 ---
 
 # Ascites Segmentation with nnUNet
 
+This model was trained as part of the research 'Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification' ([Paper](https://doi.org/10.1148/ryai.230601), [arXiv](https://arxiv.org/abs/2406.15979)). 
+
 ## Method 1: Run Inference using `nnunet_predict.py`
 
 1. Install [nnUNet_v1](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1#installation) and [PyTorch](https://pytorch.org/get-started/locally/).
@@ -105,8 +108,6 @@ user@machine:~/ascites_segmentation$ sudo docker run \
 nnunet_docker /bin/sh inference.sh
 ```
 
-
-
 - `--gpus` parameter:
   - `0, 1, 2, ..., n` for integer number of GPUs 
   - `all` for all available GPUs on the system
@@ -117,3 +118,17 @@ nnunet_docker /bin/sh inference.sh
   - `INPUT_FOLDER` contains all `.nii.gz` volumes to be predicted
   - predicted results will be written to `OUTPUT_FOLDER`
 
+## Citation
+
+If you find this repository helpful in your research, please consider citing our paper:
+
+```text
+@article{hou2024deep,
+  title={Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification},
+  author={Hou, Benjamin and Lee, Sung-Won and Lee, Jung-Min and Koh, Christopher and Xiao, Jing and Pickhardt, Perry J. and Summers, Ronald M.}
+  journal={Radiology: Artificial Intelligence},
+  pages={e230601},
+  year={2024},
+  publisher={Radiological Society of North America}
+}
+```