Abstract
The accuracy segmentation of renal cancer structure (included kidney, renal tumor, renal vein and renal artery) in computed tomography angiography (CTA) images has great clinical significance in clinical diagnosis. In this work, we designed a network architecture based on 3D U-Net and introduced the residual block into network architecture for renal cancer structure segmentation in CTA images. In the network architecture we designed, the multi-scale anisotropic convolution block, dual activation attention block and multi-scale deep supervision equipped for the better segmentation performance. We trained and validated our network in the training set, and tested our network in the opening testing set and closed testing set of KiPA22 challenge. Our method ranked the first place in the KiPA22 challenge leaderboard, and the Hausdorff Distance (HD) of kidney, renal tumor, vein and artery achieved the state-of-the-art, also Dice Similarity Coefficient (DSC) and Average Hausdorff Distance (AVD) of renal artery. According to the results in the KiPA22 challenge, our method have a better segmentation performance in CTA images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sung, W.W., Ko, P.Y., Chen, W.J., Wang, S.C., Chen, S.L.: Trends in the kidney cancer mortality-to-incidence ratios according to health care expenditures of 56 countries. Sci. Rep. 11, 1479 (2021). https://doi.org/10.1038/s41598-020-79367-y
Rossi, S.H., Prezzi, D., Kelly-Morland, C., Goh, V.: Imaging for the diagnosis and response assessment of renal tumours. World J. Urol. 36, 1927–1942 (2018). https://doi.org/10.1007/s00345-018-2342-3
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
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
He, Y., et al.: Meta grayscale adaptive network for 3D integrated renal structures segmentation. Med. Image Anal. 71, 102055 (2021)
He, Y., et al.: Dense biased networks with deep priori anatomy and hard region adaptation: semi-supervised learning for fine renal artery segmentation. Med. Image Anal. 63, 101722 (2020)
Shao, P., et al.: Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes. Eur. Urol. 59, 849–855 (2011)
Shao, P., et al.: Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy. Eur. Urol. 62, 1001–1008 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Weng, X., Hu, Z., Yang, F. (2023). A Segmentation Network Based on 3D U-Net for Automatic Renal Cancer Structure Segmentation in CTA Images. In: Xiao, Y., Yang, G., Song, S. (eds) Lesion Segmentation in Surgical and Diagnostic Applications. CuRIOUS KiPA MELA 2022 2022 2022. Lecture Notes in Computer Science, vol 13648. Springer, Cham. https://doi.org/10.1007/978-3-031-27324-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-27324-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27323-0
Online ISBN: 978-3-031-27324-7
eBook Packages: Computer ScienceComputer Science (R0)