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license: cc-by-sa-4.0
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license: cc-by-sa-4.0
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To use the model, unzip nnUNetTrainer__nnUNetPlans__2d.zip and follow the instuctions in inference_instructions.txt.
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For more information on the model, see the documentation of nnU-Net.
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This 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.
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This model is trained on an augmented version of 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
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Code and nformation about the augmentations can be found here: https://github.com/GillesVanDeVyver/EchoGAINS
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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
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