Medical image segmentation technology based on the SC-UNet model

Xuhui Zhu; Bo Han; Dongliang Li

doi:10.1117/12.3035093

19 July 2024 Medical image segmentation technology based on the SC-UNet model

Xuhui Zhu, Bo Han, Dongliang Li

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130M (2024) https://doi.org/10.1117/12.3035093
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

This paper proposes a new neural network model, series connection UNet (SC-UNet). The network model is composed of two series of UNet modules. The residual module and the attention mechanism gate are added to the model to make the back-propagation faster and focus the model's attention on the target region. In the medical image segmentation task of lung X-ray pictures, compared with the single UNet model, the average crossover ratio, mean pixel accuracy, average accuracy rate, and mean recall rate of the model structure increased by 4.51%, 3.98%, 0.45%, and 4.00% respectively. Experiments show that the SC-UNet model has significant advantages over the UNet model in medical image segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuhui Zhu, Bo Han, and Dongliang Li "Medical image segmentation technology based on the SC-UNet model", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130M (19 July 2024); https://doi.org/10.1117/12.3035093

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