Research on segmentation algorithm of liver and tumor

Liang Zhao; Haifeng Wang; Xiaodong Cheng

doi:10.1117/12.3011570

7 December 2023 Research on segmentation algorithm of liver and tumor

Liang Zhao, Haifeng Wang, Xiaodong Cheng

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294111 (2023) https://doi.org/10.1117/12.3011570
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Liver cancer is one of the common diseases. It is of great significance to realize the automatic and accurate segmentation of liver and tumors in clinical medicine. The boundary blurring and the difficulty of feature extraction in CT images of liver and tumors are perplex, an improved medical segmentation model SKE-Unet++based on Unet++ was proposed. The SKE-Unet++ model adopts the connection mode of full-scale feature fusion and integrates coarse-grained semantics and fine-grained semantic information extraction features at full scale. In order to extract more important features after convolution, the SE (Squeeze Excitation) module is added to enhance the channel features of the input feature map. The foreground sample in CT image is much smaller than the background sample, cross entropy loss function and dice loss function are combined to solve the class imbalance problem. Compared with the SKE-Unet++ model, the Dice, Jaccard and ASSD evaluation indexes of liver segmentation task in Lits data set increased by 1.03%, 0.29% and 0.2897mm respectively, and the three indexes of liver tumor segmentation task increased by 7.54%, 9.33% and 1.2077mm respectively, which confirmed the validity of the model, and provided reference for automatic segmentation of liver cancer medical images.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liang Zhao, Haifeng Wang, and Xiaodong Cheng "Research on segmentation algorithm of liver and tumor", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294111 (7 December 2023); https://doi.org/10.1117/12.3011570

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KEYWORDS

Liver

Image segmentation

Tumors

Semantics

Computed tomography

Data modeling

Feature fusion

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