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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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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DOI: https://doi.org/10.1007/978-3-031-27324-7_3
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