Abstract
The kidney cancer is one of the most common cancer types, which treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution, especially in an automated way that saves time and does not require skilled clinicians. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we combine these two networks, and further boost the performance by mixup augmentation. With mentioned approach we achieved 10th place in the KiPA2022 challenge.
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Acknowledgements
This work was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences under contract VEGA 1/0327/20.
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Hresko, D.J., Kurej, M., Gazda, J., Drotar, P. (2023). Ensembled Autoencoder Regularization for Multi-structure Segmentation for Kidney Cancer Treatment. 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_6
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DOI: https://doi.org/10.1007/978-3-031-27324-7_6
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