Abstract
The morphometry of a renal tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between renal tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Thus, we proposed an automatic kidney segmentation method, called SegKGAN. The proposed method comprises a fully convolutional generation network of densely connected blocks and a discrimination network with multi-scale feature extraction. The objective function is optimized using mean absolute error and the dice coefficient. Compared with U-Net, FCN, and SegAN, SegKGAN achieved the highest DSC value of 92.28%, the lowest VOE value of 16.17%, the lowest ASD values of 0.56 mm. Our experimental results show that the SegKGAN model have the potential to improve the accuracy of CT-based kidney segmentation.
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References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–24 (2018)
Parkin, D.M., Bray, M.F., Ferlay, M.J., et al.: Global cancer statistics, 2002. CA Cancer J. Clin. 55(2), 74 (2005)
Gao, Y., Wang, B.: An automatic kidney segmentation from abdominal CT images. In: Proceedings of the IEEE international conference on intelligent computing & intelligent systems (2010)
Abirami, M.S., Sheela, T.: Kidney segmentation for finding its abnormalities in abdominal CT images. Int. J. Appl. Eng. Res. 10(12), 32025–32034 (2015)
Khalifa, F., Soliman, A., Dwyer, A.C., et al.: A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images. In: Proceedings of the IEEE International Conference on Image Processing (2016)
Song, H., Kang, W., Zhang, Q., et al.: Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm. BMC Syst. Biol. 9(Suppl 5): S5 (2015)
Litjens, G., Kooi, T., Bejnordi, B.E., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Zhu, W., Huang, Y., et al.: AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med. Phys. 46(2), 576–589 (2019)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE transactions on pattern analysis & machine intelligence (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. Springer International Publishing (2015)
Sharma, K., Rupprecht, C., Caroli, A., et al.: Automatic segmentation of kidneys using Deep Learning for total kidney volume quantification in autosomal dominant polycystic kidney disease. Sci. Rep. 7(1), 2049 (2017)
Ruan, Y., Li, D., Marshall, H., et al.: MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network. Med. Image Anal. 64 (2020)
Sandfort, V., Yan, K., Pickhardt, P.J., et al.: Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci. Rep. 9(1) (2019)
Phca, B., Aek, C., Clgd, E., et al.: Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks. Artif. Intell. Med. 117, 102109 (2021)
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Shan, T., Song, G., Zhao, Y. (2022). Automatic Segmentation of Kidney Computed Tomography Images Based on Generative Adversarial Networks. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_21
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DOI: https://doi.org/10.1007/978-3-031-13841-6_21
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