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2022

Extending nnU-Net is all you need

Isensee, Fabian, Ulrich, Constantin, Wald, Tassilo et al.

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Semantic segmentation is one of the most popular research areas in medical image computing.

Perhaps surprisingly, despite its conceptualization dating back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions for a broad variety of segmentation problems and is regularly used as a development framework for challenge-winning algorithms.
Here we use nnU-Net to participate in the AMOS2022 challenge, which comes with a unique set of tasks: not only is the dataset one of the largest ever created and boasts 15 target structures, but the competition also requires submitted solutions to handle both MRI and CT scans.
Through careful modification of nnU-net's hyperparameters, the addition of residual connections in the encoder and the design of a custom postprocessing strategy, we were able to substantially improve upon the nnU-Net baseline.

Extending nnU-Net is all you need · Around

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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science, Springer International Publishing (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2016-December, pp. 770–778. IEEE Computer Society (dec 2016)
2016
Earlier work this paper cites.
Full, P.M., Isensee, F., Jäger, P.F., Maier-Hein, K.: Studying robustness of semantic segmentation under domain shift in cardiac mri. In: International Workshop on Statistical Atlases and Computational Models of the Heart. pp. 238–249. Springer (2020)
2020
Earlier work this paper cites.

Similar

Isensee, F., Jäger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnu-net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop. pp. 118–132. Springer (2020)
2020
Cited alongside, same era.
Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18(2)
2021
Cited alongside, same era.

Then

Ma, J.: Cutting-edge 3d medical image segmentation methods in 2020: Are happy families all alike? arXiv preprint arXiv:2101.00232 (2021)
Original
2021
Later among the works it cites.
Ji, Y., Bai, H., Yang, J., Ge, C., Zhu, Y., Zhang, R., Li, Z., Zhang, L., Ma, W., Wan, X., et al.: Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. arXiv preprint arXiv:2206.08023 (2022)
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2022
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