onnx license:cc-by-4.0 region:us
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license: cc-by-4.0
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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 on the first split of the CAMUS dataset.

The model can be directly used in the framework provided at
https://github.com/gillesvntnu/GCN_UNET_agreement_demo.git

Information on the splits of the CAMUS dataset can be found at:
https://github.com/gillesvntnu/GCN_multistructure/tree/main/files/subgroups_CAMUS

For the corresponding version in .pth format, see
https://huggingface.co/gillesvdv/nnunet_camus_cv1