Abstract
Kidney cancer is one of the most common malignancies worldwide. Early diagnosis is an effective way to reduce the mortality and automated segmentation of kidney tumor in computed tomography scans is an important way to assisted kidney cancer diagnosis. In this paper, we propose a convolution-and-transformer network (COTRNet) for end to end kidney, kidney tumor, and kidney cyst segmentation. COTRNet is an encoder-decoder architecture where the encoder and the decoder are connected by skip connections. The encoder consists of four convolution-transformer layers to learn multi-scale features which have local and global receptive fields crucial for accurate segmentation. In addition, we leverage pretrained weights and deep supervision to further improve segmentation performance. Experimental results on the 2021 kidney and kidney tumor segmentation (kits21) challenge demonstrated that our method achieved average dice of 61.6%, surface dice of 49.1%, and tumor dice of 50.52%, respectively, which ranked the \(22_{th}\) place on the kits21 challenge.
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Acknowledgment
This work was supported by the Fujian Provincial Natural Science Foundation project (Grant No. 2021J02019, 2020J01472).
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Shen, Z., Yang, H., Zhang, Z., Zheng, S. (2022). Automated Kidney Tumor Segmentation with Convolution and Transformer Network. 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_1
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