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- W3184189121 abstract "In this paper, we propose a new semantic segmentation network called ’E-Res U-Net’, to achieve better segmentation results of deep and superficial muscles in ultrasonic muscle images. This model is based on U-Net, and its structure has been modified to improve the performance of the algorithm. There are three aspects of improvement based on U-Net, including E-Res layer, dilated convolution module, and E-Res path. Additional experiments demonstrate that each designed module in our proposed network is effective, can improve the accuracy compared to the original U-Net. When compared with other algorithms which are state-of-the-art, the experimental result under the overall network structure is even more excellent. • Segmentation of deep and superficial muscles in ultrasonic muscle images. • An improved U-Net model according with three designed module is presented. • Each designed module can effectively improve the accuracy of segmentation. • Compared with other model, the segmentation effect of is significantly improved." @default.
- W3184189121 created "2021-08-02" @default.
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- W3184189121 date "2021-12-01" @default.
- W3184189121 modified "2023-09-24" @default.
- W3184189121 title "E-Res U-Net: An improved U-Net model for segmentation of muscle images" @default.
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- W3184189121 doi "https://doi.org/10.1016/j.eswa.2021.115625" @default.
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