Abstract
Automatic kidney and tumor segmentation from CT volumes is essential for clinical diagnosis and surgery planning. However, it is still a very challenging problem as kidney and tumor usually exhibit various scales, irregular shapes and blurred contours. In this paper, we propose a memory efficient automatic kidney and tumor segmentation algorithm based on non-local context guided 3D U-Net. Different from the traditional 3D U-Net, we implement a lightweight 3D U-Net with depthwise separable convolution (DSC), which can not only avoid over fitting but also improve the generalization ability. By encoding long range pixel-wise dependencies in features and recalibrating the weight of channels, we also develop a non-local context guided mechanism to capture global context and fully utilize the long range dependencies during the feature selection. Thanks to the non-local context guidance (NCG), we can successfully complement high-level semantic information with the spatial information simply based on a skip connection between encoder and decoder in the 3D U-Net, and finally realize a more accurate 3D kidney and tumor segmentation network. Our proposed method was validated and evaluated with KiTS dataset, including various 3D kidney and tumor patient cases. Convincing visual and statistical results verified effectiveness of our method. Comparisons with state-of-the-art methods were also conducted to demonstrate its advantages in terms of both efficiency and accuracy.
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References
Ali, A.M., Farag, A.A., El-Baz, A.S.: Graph cuts framework for kidney segmentation with prior shape constraints. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4791, pp. 384–392. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75757-3_47
Brügger, R., Baumgartner, C.F., Konukoglu, E.: a partially reversible u-net for memory-efficient volumetric image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 429–437. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_48
Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: GCNet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)
Chen, C., Liu, X., Ding, M., Zheng, J., Li, J.: 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 184–192. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_21
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251–1258 (2017)
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
Crum, W.R., Camara, O., Hill, D.L.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging (TMI) 25(11), 1451–1461 (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034 (2015)
Heller, N., et al.: The kits19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes (2019)
Howard, A., et al.: Searching for MobileNetV3. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1314–1324 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations (ICLR) (2015)
Lin, D.T., Lei, C.C., Hung, S.W.: Computer-aided kidney segmentation on abdominal CT images. IEEE Trans. Inf Technol. Biomed. 10(1), 59–65 (2006)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. In: Conference on Medical Imaging with Deep Learning (MIDL) (2018)
Rickmann, A.-M., Roy, A.G., Sarasua, I., Navab, N., Wachinger, C.: ‘Project & Excite’ modules for segmentation of volumetric medical scans. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 39–47. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_5
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7794–7803 (2018)
Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.A.: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: AAAI Conference on Artificial Intelligence (2017)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)
Acknowledgement
This work was supported in part by grants from the National Natural Science Foundation of China (No. 61973221), the Natural Science Foundation of Guangdong Province, China (Nos. 2018A030313381 and 2019A1515011165), the Major Project or Key Lab of Shenzhen Research Foundation, China (Nos. JCYJ2016060 8173051207, ZDSYS201707311550233, KJYY201807031540021294 and JSGG201 805081520220065), the COVID-19 Prevention Project of Guangdong Province, China (No. 2020KZDZX1174), the Major Project of the New Generation of Artificial Intelligence (No. 2018AAA0102900) and the Hong Kong Research Grants Council (Project No. PolyU 152035/17E and 15205919).
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Li, Z., Pan, J., Wu, H., Wen, Z., Qin, J. (2020). Memory-Efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_20
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DOI: https://doi.org/10.1007/978-3-030-59719-1_20
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