Abstract
In this paper, we propose an algorithm framework for kidney segmentation in ultrasound images. Due to the characteristics of kidney ultrasound images, such as high noise, heterogeneous structure, low contrast, multiple artifacts, and relatively fixed shape, accurately segmenting clear and complete kidney structures from the images is still a challenging task. Our framework consists of two parts: shape aware dual-task multi-scale fusion network and self-correction. The first part uses a U-shape structure with multi-scale feature cross fusion skip connections to perform segmentation and can simultaneously predict the segmentation map and the shape-aware prediction level set function, we use a dual-task consistency module to constrain the shape of the segmentation map, enabling the network to learn the target area more accurately, through dual-task consistency supervised learning, we obtain a pre-trained model. The second part uses an iterative aggregation strategy for the pre-trained model to optimize it and reduce the noise and other issues in the prediction results. Experimental results show that our algorithm framework outperforms several state-of-the-art methods on kidney ultrasound datasets.
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
Levin, A., Stevens, P.E.: Early detection of CKD: the benefits, limitations and effects on prognosis. Journal 7(8), 446–457 (2011)
Torres, H.R., Queiros, S., Morais, P., Oliveira, B., Fonseca, J.C., Vilaca, J.L.: Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: a systematic review. Comput. Methods Programs Biomed. 157, 49–67 (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
Cao, H., et al.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13803, pp. 205–218. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-25066-8_9
Xie, J., Jiang, Y., Tsui, H.: Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans. Med. Imaging 24(1), 45–57 (2005). https://doi.org/10.1109/TMI.2004.837792
Sandmair, M., Hammon, M., Seuss, H., Theis, R., Uder, M., Janka, R.: Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding. BMC. Res. Notes 9(1), 1–10 (2016)
Marsousi, M., Plataniotis, K.N., Stergiopoulos, S.: An automated approach for kidney segmentation in three-dimensional ultrasound images. IEEE J. Biomed. Health Inform. 21(4), 1079–1094 (2017). https://doi.org/10.1109/JBHI.2016.2580040
Mendoza, C.S., Kang, X., Safdar, N., Myers, E., Peters, C.A., Linguraru, M.G.: Kidney segmentation in ultrasound via genetic initialization and Active Shape Models with rotation correction. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA, USA, pp. 69–72 (2013). https://doi.org/10.1109/ISBI.2013.6556414
Jokar, E., Pourghassem, H., Linguraru: Kidney segmentation in ultrasound images using curvelet transform and shape prior. In: 2013 International Conference on Communication Systems and Network Technologies, Gwalior, India, pp. 180–185 (2013). https://doi.org/10.1109/CSNT.2013.47
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 (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Ravishankar, H., Venkataramani, R., Thiruvenkadam, S., Sudhakar, P., Vaidya, V.: Learning and incorporating shape models for semantic segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 203–211. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_24
Jackson, P., Hardcastle, N., Dawe, N., Kron, T., Hofman, M.S., Hicks, R.J.: Deep learning kidney segmentation for fully automated radiation dose estimation in unsealed source therapy. Front. Oncol. 8, 215 (2018)
Weerasinghe, N.H., Lovell, N.H., Welsh, A.W., Stevenson, G.N.: Multi-parametric fusion of 3D power Doppler ultrasound for fetal kidney segmentation using fully convolutional neural networks. IEEE J. Biomed. Health Inform. 25(6), 2050–2057 (2020)
Chen, J., et al.: TransuNet: transformers make strong encoders for medical image segmentation. arXiv preprint. arXiv:2102.04306 (2021)
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)
Zhuang, X., et al.: Residual Swin transformer Unet with consistency regularization for automatic breast ultrasound tumor segmentation. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3071–3075(2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: 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
Wang, H., Cao, P., Wang, J., Zaiane, O.R.: UctransNet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2441–2449 (2022)
Yin, S., et al.: Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Med. Image Anal. 60, 101602 (2020)
Sun, J., Darbehani, F., Zaidi, M., Wang, B.: SAUNet: shape attentive U-net for interpretable medical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 797–806. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_77
Ma, J., et al.: How distance transform maps boost segmentation CNNs: an empirical study. In: Medical Imaging with Deep Learning, pp. 479–492. PMLR (2020)
Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_54
Xue, Y., et al.: Shape-aware organ segmentation by predicting signed distance maps. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 12565–12572 (2020)
Luo, X., Chen, J., Song, T., Wang, G., Huang, X.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, No. 10, pp. 8801–8809 (2021)
Zou, H., Gong, X., Luo, J., Li, T.: A robust breast ultrasound segmentation method under noisy annotations. Comput. Methods Programs Biomed. 209, 106327 (2021)
Li, P., Xu, Y., Wei, Y., Yang, Y.: Self-correction for human parsing. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3260–3271 (2020)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 430–436 (2005)
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)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint. arXiv:1611.01144 (2016)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Song, Z., Liu, X., Gong, Y., Hao, T., Zeng, K. (2023). A Two-Stage Framework for Kidney Segmentation in Ultrasound Images. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-5847-4_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5846-7
Online ISBN: 978-981-99-5847-4
eBook Packages: Computer ScienceComputer Science (R0)