An improved liver tumor image segmentation method based on mixed domain attention mechanism

Shijie Pan; Abdujelil Abdurahman; Aishan Wumaier; Dong Li; Junhua Zhong; Zulin Chen

doi:10.1117/12.2636813

29 April 2022 An improved liver tumor image segmentation method based on mixed domain attention mechanism

Shijie Pan, Abdujelil Abdurahman, Aishan Wumaier, Dong Li, Junhua Zhong, Zulin Chen

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122470A (2022) https://doi.org/10.1117/12.2636813
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

This paper proposes a new liver tumor segmentation method based on mixed domain attention mechanism. Firstly, by combining the well-known SENet with channel attention architecture, cross channels are interacted at their Excitations by using multiple one-dimensional convolution kernels. Then, multiple dilation convolution is used to increase the receptive field in the spatial attention module of BAM. After then, the channel and spatial attention feature maps are fused to recorrect the original feature map. Finally, a gating mechanism is introduced at the sampling skip connection on the decoder to filter important features. The experimental results show that in many evaluation indexes, the accuracy of this method is higher than the relevant segmentation methods, and thus the introduced segmentation method can provide some guidance in clinical diagnosis for liver tumor.

Citation Download Citation

Shijie Pan, Abdujelil Abdurahman, Aishan Wumaier, Dong Li, Junhua Zhong, and Zulin Chen "An improved liver tumor image segmentation method based on mixed domain attention mechanism", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122470A (29 April 2022); https://doi.org/10.1117/12.2636813

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KEYWORDS

Convolution

Image segmentation

Liver

Tumors

Feature extraction

Network architectures

Convolutional neural networks

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