TA-UNet3+: a transformer-based method for kidney tumor segmentation

Xiqing Hu

doi:10.1117/12.2678615

14 April 2023 TA-UNet3+: a transformer-based method for kidney tumor segmentation

Xiqing Hu

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340D (2023) https://doi.org/10.1117/12.2678615
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

Kidney tumors are among the ten most common tumors in humans. Precise resection of renal tumors has become an essential means of tumor treatment. Accurate kidney segmentation in CT images is a prerequisite for surgery, and segmenting kidneys and kidney tumors is challenging. At present, most segmentation methods use traditional convolutional neural networks. This paper uses a visual transformer to replace the encoder part of the neural network and innovatively adds a new attention mechanism, encoder-decoder transformer (EDformer), to the skip connection to learn local features. We also adopted a new type of skip connection to integrate low-level semantic features with high-level semantic features as much as possible. I named our method TAU-Net3+. Based on the experimental results of CT images of 300 patients, our proposed method can detect kidney and renal tumors with the highest accuracy. The mean dice coefficients of kidney and kidney tumors obtained by this method are 0.9885 and 0.8638, respectively, which are higher than the other three advanced segmentation methods.

Citation Download Citation

Xiqing Hu "TA-UNet3+: a transformer-based method for kidney tumor segmentation", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340D (14 April 2023); https://doi.org/10.1117/12.2678615

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KEYWORDS

Kidney

Tumors

Image segmentation

Transformers

Computed tomography

Medical imaging

Convolutional neural networks

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