Abstract
Kidney cancer is one of the most common cancers. Precise delineation and localization of the lesion area play a crucial role in the diagnosis and treatment of kidney cancer. Deep learning-based automatic medical image segmentation can help to confirm the diagnosis. The traditional 3D nnU-net based on convolutional layers is widely used in medical image segmentation. However, the fixed receptive field of convolutional neural networks introduces an induction bias limiting their ability to capture long-range spatial information in input images. The Swin Transformer addresses this limitation by leveraging the global contextual modeling ability obtained through self-attention computation. However, it requires a large amount of training data and lacks in local feature encoding. To overcome these limitations, our paper proposes a hybrid network structure called STransUnet, which combines the nnU-net with Swin Transformer. STransUnet retains the local feature encoding capability of nnU-net while introducing the Swin Transformer to capture a broader range of global contextual information, resulting in a more powerful modeling ability for image segmentation tasks. In the KiTS23 challenge, our average Dice and average Surface Dice of segmentation on the test are 0.801 and 0.680 ranked the 6th and 8th respectively and our Tumor Dice is 0.687.
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This work is supported by the National Key Research and Development Program of China (Grant no: 2021YFC2009200).
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Qian, L., Luo, L., Zhong, Y., Zhong, D. (2024). A Hybrid Network Based on nnU-Net and Swin Transformer for 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_5
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DOI: https://doi.org/10.1007/978-3-031-54806-2_5
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