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• W3201321351 abstract "This paper focuses on uterine segmentation, an important clue for understanding MRI images and medical analysis of expectant mothers, which has long been underestimated. Related works have proven that the receptive field is crucial in computer vision. However, current methods usually use pooling operations to continuously enlarge the receptive field, which leads to some inevitable information loss. In this paper, we design the Dynamic Selection Module (DSM) to effectively capture the spatial perception of medical images. Specifically, DSM adopts dynamic convolution kernel to adaptively adjust the receptive field in the horizontal and vertical directions. We then combine DSM and residual block to construct a Dynamic Residual Unit (DRU) which further learns feature representation. Then DRU is embedded in the standard U-Net termed Dynamic Selection Network (DSNet). We evaluate our method on the Uterus dataset we acquired. To validate the generalization of this method, we also do the same experiment on the Gland Segmentation dataset and Lung dataset. The results demonstrate that DSNet can significantly boost the performance of medical image segmentation than other related encoder-decoder architectures." @default.
• W3201321351 created "2021-09-27" @default.
• W3201321351 creator A5007977293 @default.
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• W3201321351 date "2021-01-01" @default.
• W3201321351 modified "2023-09-26" @default.
• W3201321351 title "DSNet: Dynamic Selection Network for Biomedical Image Segmentation" @default.
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• W3201321351 doi "https://doi.org/10.1007/978-3-030-86365-4_50" @default.
• W3201321351 hasPublicationYear "2021" @default.
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