Abstract
Abdominal organ segmentation has been used in many important clinical applications, however, cases with accurate labels require huge manual labour and financial resources. As a potential alternative, semi-supervised learning can explore useful information from unlabeled cases, with only few labeled cases involved. Therefore, we propose our baseline model using augmented 3D-UNet and adopt semi-supervised method–Mean Teacher, to make quantitative evaluation on the FLARE2022 validation cases. Our method achieves average dice similarity coefficient (DSC) of 62.16\(\%\), Normalized Surface Distance (NSD) of 62.27\(\%\), running time of 9.58 s, and AUC of GPU and CPU is only 7424 and 199 respectively, which surpasses almost all other teams on resource consumption, demonstrating the effectiveness of our methods.
Z. Chen and T. Wang—Equal contribution
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, pp. 2613–2622 (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
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)
Lin, T.Y., Goyal, P., Girshick, R.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Ma, J., et al.: AbdomenCT-1K: is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695–6714 (2022)
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)
Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic segmentation using generative adversarial network. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems (2017)
Acknowledgements
The authors of this paper 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.
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
Chen, Z., Wang, T., Han, S., Song, Y., Li, S. (2022). Semi-supervised Augmented 3D-CNN for FLARE22 Challenge. 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_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-23911-3_6
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)
