Abstract
Kidney cancer has been recognized as one of the top ten prevalent neoplastic conditions, ranking as the third most frequent malignant tumor within the genitourinary system. Its high mortality rates pose a significant risk to human health. Accurate and automated segmentation of the kidneys, kidney tumors, and kidney cysts in CT scans is of paramount importance, as it provides medical professionals with valuable assistance in their diagnostic and therapeutic efforts. In KiTS23, this work presents a novel two-stage cascaded framework based on the nnU-Net architecture. Between the two stages of the cascaded network, a cropping process is implemented. This process involves extracting a region of interest (ROI) that encompasses the kidneys from the initial segmentation. The extracted ROI is subsequently utilized as input for the second stage, facilitating more focused and refined segmentation of kidney tumors and cysts. Furthermore, to tackle the inherent challenge of class imbalance, Focal Loss is employed as a mitigation strategy. The network achieved average Sørensen-Dice scores of 0.933, 0.709, and 0.645 for the classes kidney, masses and tumor respectively. Similarly, the average surface Dice scores for these classes were 0.866, 0.545, and 0.490. This led to the 18th position in the KiTS23 challenge.
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
The 2023 kidney and kidney tumor segmentation challenge. https://kits-challenge.org/kits23/
Chhikara, B.S., Parang, K.: Global Cancer Statistics 2022: the trends projection analysis. Chem. Biol. Lett. 10(1), 451–451 (2023)
Heller, N., et al.: The KiTS21 challenge: automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT (2023)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Zhao, Z., Chen, H., Wang, L.: A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds.) KiTS 2021. LNCS, vol. 13168, pp. 53–58. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98385-7_8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Dai, Y., Zhang, J., Yin, J. (2024). Cascaded nnU-Net for Kidney and Kidney Tumor Segmentation. In: Heller, N., et al. Kidney and Kidney Tumor Segmentation. KiTS 2023. Lecture Notes in Computer Science, vol 14540. Springer, Cham. https://doi.org/10.1007/978-3-031-54806-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-54806-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54805-5
Online ISBN: 978-3-031-54806-2
eBook Packages: Computer ScienceComputer Science (R0)