Abstract
Accurate segmentation of kidney tumours can help doctors diagnose the disease. In this work, we described a multi-stage 2.5D semantic segmentation networks to automatically segment kidney and tumor and cyst in computed tomography (CT) images. First, the kidney is pre-segmented by the first stage network ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-processing operation based on a 3D connected region is used for the removal of false-positive segmentation results. We evaluate this approach in the KiTS21 challenge, which shows promising performance.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62071379, 62071378 and 61571361), the Natural Science Basic Research Plan in Shaanxi Province of China (Grant Nos. 2021JM-461 and 2020JM-299), the Fundamental Research Funds for the Central Universities (Grant No. GK201903092), and New Star Team of Xi'an University of Posts & Telecommunications (Grant No. xyt2016-01).
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Chen, Z., Liu, H. (2022). 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds) Kidney and Kidney Tumor Segmentation. KiTS 2021. Lecture Notes in Computer Science, vol 13168. Springer, Cham. https://doi.org/10.1007/978-3-030-98385-7_4
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DOI: https://doi.org/10.1007/978-3-030-98385-7_4
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