Abstract
In order to make full use of unlabeled images, we developed a pseudo-label based localization-to-segmentation framework for efficient abdominal organs segmentation. To reduce the target region, we locate the abdomen by a U-Net, then we train a fine organ segmentation model, which reduce the maximum usage of RAM memory. Segmentation with Dual-decoders is designed to improve the stability and cross supervise each other by pseudo labels. We also propose a class-weighted loss to pay more attention on the small organs like gallbladder, pancreas, which improve the mean DSC. Finally, we test the models on the public validation set, the total running time for the 50 CT images is 6676 s, the mean DSC is 0.8830 and the mean NSD is 0.9189.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2613–2622 (2021)
Clark, K., et al.: The cancer imaging archive (tcia): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)
Heller, N., et al.: An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging. Proc. Am. Soc. Clin. Oncol. 38(6), 626 (2020)
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., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)
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.: Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3100536
Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. IEEE (2020)
Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)
Yang, X., Song, Z., King, I., Xu, Z.: A survey on deep semi-supervised learning. CoRR abs/2103.00550 (2021). https://arxiv.org/abs/2103.00550
Zoph, B., et al.: Rethinking pre-training and self-training. CoRR abs/2006.06882 (2020). https://arxiv.org/abs/2006.06882
Acknowledgements
We declare that the segmentation method they implemented for participation in the FLARE 2022 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, E., Zhao, Y., Wu, Y. (2022). Cascade Dual-decoders Network for Abdominal Organs Segmentation. 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_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-23911-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23910-6
Online ISBN: 978-3-031-23911-3
eBook Packages: Computer ScienceComputer Science (R0)
