Abstract
In this paper, we combine the advantages of convolution local correlation and translation invariance in CNN with Transformer’s ability to effectively capture long-term dependencies between pixels to produce high-quality pseudo labels. In order to segment images efficiently and quickly, we select nnU-Net [2] as the final segmentation network and use pseudo labels, unlabeled data and labeled data together to train the network, and then we use Generic U-Net [2], the backbone network of nnU-Net, as final prediction network. The mean DSC of the prediction results of our method on validation set of FLARE2022 Challenge [3] is 0.7580.
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References
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale (2020). https://doi.org/10.48550/ARXIV.2010.11929. https://arxiv.org/abs/2010.11929
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Ma, J., Wang, B., Bharadwaj, S.: FLARE2022 challenge (2022). https://flare22.grand-challenge.org/
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows (2021). https://doi.org/10.48550/ARXIV.2103.14030. https://arxiv.org/abs/2103.14030
Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022). https://doi.org/10.1016/j.media.2022.102616
Ma, J., et al.: AbdomenCT-1K: is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695–6714 (2022)
Yushkevich, P., Gerig, G., Bharadwaj, S.: ITK-SNAP (2022). http://www.itksnap.org/
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)
Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: nnFormer: interleaved transformer for volumetric segmentation (2021). https://doi.org/10.48550/ARXIV.2109.03201. https://arxiv.org/abs/2109.03201
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Xin, R., Wang, L. (2022). Abdominal Multi-organ Segmentation Using CNN and Transformer. In: Ma, J., Wang, B. (eds) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. FLARE 2022. Lecture Notes in Computer Science, vol 13816. Springer, Cham. https://doi.org/10.1007/978-3-031-23911-3_24
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DOI: https://doi.org/10.1007/978-3-031-23911-3_24
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