Abstract
Abdominal computed tomography is frequently used to non-invasively map local conditions and to detect any benign or malign masses. However, ill-defined borders of malign objects, fuzzy texture, and time pressure in fact, make accurate segmentation in clinical settings a challenging task. In this paper, we propose a two-stage deep learning architecture for kidney and kidney masses segmentation, denoted as convolutional computer tomography network (CCTNet). The first stage locates volume bounding box containing both kidneys. The second stage performs the segmentation of kidney, kidney tumors and cysts. In the first stage, we use a pre-trained 3D low resolution nnU-Net. In the second stage, we employ a mixup augmentation to improve segmentation performance of the second 3D full resolution nnU-Net. The obtained results indicate that CCTNet can provide improved segmentation of kidney, kidney tumor and cyst.
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Acknowledgment
This work was supported by the Slovak Research and Development Agency under contract No. APVV-16-0211.
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Gazda, M., Bugata, P., Gazda, J., Hubacek, D., Hresko, D.J., Drotar, P. (2022). Mixup Augmentation for Kidney and Kidney Tumor Segmentation. 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_12
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DOI: https://doi.org/10.1007/978-3-030-98385-7_12
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