Abstract
3D fine renal artery segmentation on abdominal CTA image targets on the segmentation of the complete renal artery tree which will help clinicians locate the interlobar artery’s corresponding blood feeding region easily. However, it is still a challenging task that no one has reported success due to the large intra-scale changes, large inter-anatomy variation, thin structures, small volume ratio and limitation of labeled data. Hence, in this paper, we propose a novel semi-supervised learning framework named DPA-DenseBiasNet for 3D fine renal artery segmentation. The dense biased connection method is presented for multi-receptive field feature maps merging and implicit deep supervision [5] which enable the network to adapt to large intra-scale changes and improve its training process. The dense biased network (DenseBiasNet) is designed based on this method. We develop deep priori anatomy (DPA) for semi-supervised learning of thin structures. Differ from other semi-supervised methods, it embeds priori anatomical features to segmentation network which avoids inaccurate results sensitive to thin structures as optimizing targets, so that the network achieves generalization of different anatomies with the help of unlabeled data. Only 26 labeled and 118 unlabeled images were used to train our framework and it achieves satisfactory results on the testing dataset. The mean centerline voxel distance is 1.976 which reduced by 3.094 compared to 3D U-Net. The results illustrate that our framework has great prospects in the diagnosis and treatment of kidney disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cancer stat facts: Kidney and renal pelvis cancer. https://seer.cancer.gov/statfacts/html/kidrp.html. Accessed 12 Feb 2019
Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_29
Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 311–319. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_36
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
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. Eprint Arxiv, pp. 562–570 (2014)
Li, J., Lo, P., Taha, A., Wu, H., Zhao, T.: Segmentation of renal structures for image-guided surgery. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 454–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_52
Ljungberg, B., et al.: Eau guidelines on renal cell carcinoma: 2014 update. Eur. Urol. 67(5), 913–924 (2015)
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)
Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 370–378. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_43
Petru, B., Elena, Ş., Dan, I., Klara, B., Radu, B., Constantin, D.: Morphological assessments on the arteries of the superior renal segment. Surg. radiol. Anat. 34(2), 137–144 (2012)
Shao, P., et al.: Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes. Eur. Urol. 59(5), 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(6), 1001–1008 (2012)
Taha, A., Lo, P., Li, J., Zhao, T.: Kid-net: convolution networks for kidney vessels segmentation from CT-volumes. arXiv preprint arXiv:1806.06769 (2018)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM (2008)
Zhu, X.J.: Semi-supervised learning literature survey, Technical report. University of Wisconsin-Madison Department of Computer Sciences (2005)
Acknowledgements
This research was supported by the National Key Research and Development Program of China (2017YFC0107903), National Natural Science Foundation under grants (31571001, 61828101), the Short-Term Recruitment Program of Foreign Experts (WQ20163200398), Key Research and Development Project of Jiangsu Province (BE2018749) and Southeast University-Nanjing Medical University Cooperative Research Project (2242019K3DN08).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
He, Y. et al. (2019). DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-32226-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32225-0
Online ISBN: 978-3-030-32226-7
eBook Packages: Computer ScienceComputer Science (R0)