Abstract
Kidney cancer is one of the top ten cancers in the world, and its incidence is still increasing. Early detection and accurate treatment are the most effective control methods. The precise and automatic segmentation of kidney tumors in computed tomography (CT) is an important prerequisite for medical methods such as pathological localization and radiotherapy planning, However, due to the large differences in the shape, size, and location of kidney tumors, the accurate and automatic segmentation of kidney tumors still encounter great challenges. Recently, U-Net and its variants have been adopted to solve medical image segmentation problems. Although these methods achieved favorable performance, the long-range dependencies of feature maps learned by convolutional neural network (CNN) are overlooked, which leaves room for further improvement. In this paper, we propose an squeeze-and-excitation encoder-decoder network, named SeResUNet, for kidney and kidney tumor segmentation. SeResUNet is an U-Net-like architecture. The encoder of SeResUNet contains a SeResNet to learns high-level semantic features and model the long-range dependencies among different channels of the learned feature maps. The decoder is the same as the vanilla U-Net. The encoder and decoder are connected by the skip connections for feature concatenation. We used the kidney and kidney tumor segmentation 2021 dataset to evaluate the proposed method. The dice, surface dice and tumor dice score of SeResUNet are 67.2%, 54.4%, 54.5%, respectively.
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
References
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
Dhere, A., Sivaswamy, J.: Self-supervised learning for segmentation (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 770–778 (2016)
Heller, N., et al.: The kits19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes. arXiv preprint: arXiv:1904.00445 (2019)
Hou, X., et al.: A triple-stage self-guided network for kidney tumor segmentation. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 341–344. IEEE (2020)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Isensee, F., Maier-Hein, K.H.: An attempt at beating the 3D U-Net (2019)
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)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)
Nikolov, S., et al.: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. arXiv preprint: arXiv:1809.04430 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Yang, G., et al.: Automatic kidney segmentation in CT images based on multi-atlas image registration. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5538–5541. IEEE (2014)
Yu, Q., Shi, Y., Sun, J., Gao, Y., Zhu, J., Dai, Y.: Crossbar-net: a novel convolutional neural network for kidney tumor segmentation in CT images. IEEE Trans. Image Process. 28(8), 4060–4074 (2019)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: U Net++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Acknowledgment
This work was supported by the Fujian Provincial Natural Science Foundation project (2021J02019, 2021J01578, 2019Y9070).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Wen, J., Li, Z., Shen, Z., Zheng, Y., Zheng, S. (2022). Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds) Kidney and Kidney Tumor Segmentation. KiTS 2021. Lecture Notes in Computer Science, vol 13168. Springer, Cham. https://doi.org/10.1007/978-3-030-98385-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-98385-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98384-0
Online ISBN: 978-3-030-98385-7
eBook Packages: Computer ScienceComputer Science (R0)
