Abstract
Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images has significant therapeutic implications. 3D visual model of kidney, renal tumor, renal vein and renal artery helps clinicians make accurate preoperative planning. In this paper, we utilize a modified nnU-Net named nnHra-Net network, and propose a multi-stage framework with coarse-to-fine and ensemble learning strategy to precisely segment the multi-structure of kidney. Our method is quantitatively evaluated on a public dataset from MICCAI 2022 Kidney Parsing for Renal Cancer Treatment Challenge (KiPA2022), with mean Dice similarity coefficient (DSC) as 95.91%, 90.65%, 88.60% and 85.36% for the kidneys, kidney tumors, arteries and veins respectively, wining the stable and top performance on both open and close testing in the challenge.
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References
Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A.: Cancer statistics. Cancer J. Clin. 72(1), 7–33 (2022)
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)
Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R.: Automatic detection and segmentation of kidneys in 3D CT images using random forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 66–74. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_9
Khalifa, F., Soliman, A., Dwyer, A.C., Gimel’farb, G., El-Baz, A.: A random forest-based framework for 3d kidney segmentation from dynamic contrast-enhanced CT images. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3399–3403 (2016)
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 (2014)
Wang, C., et al.: Precise renal artery segmentation for estimation of renal vascular dominant regions. In: Medical Imaging 2016: Image Processing (M.A. Styner and E.D. Angelini, eds.), vol. 9784, p. 97842M, International Society for Optics and Photonics, SPIE (2016)
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
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
Liu, Y., Zhao, Z., Wang, L.: A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment. arXiv e-prints. arXiv:2207.09165, July 2022
He, Y., et al.: Meta grayscale adaptive network for 3d integrated renal structures segmentation. Med. Image Anal. 71, 102055 (2021)
Dangi, S., Linte, C.A., Yaniv, Z.: A distance map regularized CNN for cardiac cine MR image segmentation. Med. Phys. 46(12), 5637–5651 (2019)
He, Y., et al.: Dense biased networks with deep priori anatomy and hard region adaptation: semi-supervised learning for fine renal artery segmentation. Med. Image Anal. 63, 101722 (2020)
Shit, S., Paetzold, J.C., Sekuboyina, A., Zhylka, A., Menze, B.H.: clDice - a topology-preserving loss function for tubular structure segmentation (2020)
Isensee, F., Petersen, J., Kohl, S.A.A., Jäger, P.F., Maier-Hein, K.H.: nnu-Net: breaking the spell on successful medical image segmentation. CoRR, vol. abs/1904.08128 (2019)
McClennan, B., Stanley, R., Melson, G., Levitt, R., Sagel, S.: CT of the renal cyst: is cyst aspiration necessary? Am. J. Roentgenol. 133(4), 671–675 (1979). PMID: 114010
Acknowledgment
We sincerely appreciate the organizers with the donation of KiPA2022 dataset. The authors of this paper declare that the segmentation method they implemented for participation in the KiPA2022 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention.
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Liu, Y., Zhao, Z., Wang, L. (2023). A CNN-Based Multi-stage Framework for Renal Multi-structure Segmentation. In: Xiao, Y., Yang, G., Song, S. (eds) Lesion Segmentation in Surgical and Diagnostic Applications. CuRIOUS KiPA MELA 2022 2022 2022. Lecture Notes in Computer Science, vol 13648. Springer, Cham. https://doi.org/10.1007/978-3-031-27324-7_3
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