Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT

Rongrong Bi; Chunlei Ji; Zhipeng Yang; Meixia Qiao; Peiqing Lv; Haiying Wang; Rongrong Bi; Chunlei Ji; Zhipeng Yang; Meixia Qiao; Peiqing Lv; Haiying Wang

doi:10.3934/mbe.2022219

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 4703-4718. doi: 10.3934/mbe.2022219

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Research article Special Issues

Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT

1.
Department of Software Engineering, Harbin University of Science and Technology, Rongcheng 264300, China
2.
School of Automation, Harbin University of Science and Technology, Harbin 150080, China

Academic Editor: Simon James Fong

Received: 12 January 2022 Revised: 20 February 2022 Accepted: 24 February 2022 Published: 09 March 2022

Purpose: Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT. Method: We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations. Results: We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods. Conclusions: Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.
- liver tumor,
- segmentation,
- CT,
- residual,
- attention
Citation: Rongrong Bi, Chunlei Ji, Zhipeng Yang, Meixia Qiao, Peiqing Lv, Haiying Wang. Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4703-4718. doi: 10.3934/mbe.2022219

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Abstract

Purpose: Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT. Method: We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations. Results: We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods. Conclusions: Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.

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