Update README.md

README.md

@@ -2,10 +2,12 @@
 license: mit
 ---
 
+If you use this model, you should cite the following work: Van De Vyver, Gilles, et al. "Regional quality estimation for echocardiography using deep learning." arXiv preprint arXiv:2408.00591 (2024). https://arxiv.org/abs/2408.00591
+
+nnU-Net model for cardiac segmentatoin on apical two and four chamber views, trained on the CAMUS dataset. See https://github.com/GillesVanDeVyver/arqee for usage.
+
 This model is trained on the CAMUS dataset: S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al. "Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198-2210, Sept. 2019. doi: 10.1109/TMI.2019.2900516
 
 This model uses the nnU-Net architecture: Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z
 
-This model is part of the following work. You must cite this paper for any use of the model: G.Van De Vyver, S. Thomas, G. Ben-Yosef, S. H. Olaisen, H. Dalen, L. Løvstakken, and E. Smistad: “Towards Robust Cardiac Segmentation using Graph Convolutional Networks” in arXiv preprint arXiv:2310.01210, 2023, https://github.com/gillesvntnu/GCN_multistructure.git
-
-The model is the .onnx version of the nnU-Net model as described in the work mentioned above. It is trained and validated using the official train and validation splits provided by the public CAMUS dataset.
+The model is the .onnx version of the nnU-Net model as described in the first work mentioned above. It is trained and validated using the official train and validation splits provided by the public CAMUS dataset.