Abstract: In view of the skin lesions with different scales and irregular shapes, the traditional U-Net method lacked robustness to analyze targets from different scales, and lost some spatial context information when extracting high-level semantic features of the image, which affected the accuracy of subsequent segmentation, the author proposed a medical image segmentation method based on U-Net with multi-scale and multi-dimensional feature fusion. Firstly, the spatial context information from different scales was fused by atrous convolution. Secondly, the weight information of each channel of the feature map was extracted by the channel context information fusion module. Finally, the multi-scale and multi-dimensional information in the feature map was fused to preserve more spatial context information. Experimental results show that the proposed method can segment the skin lesion on the skin lesion dataset, and the segmentation effect is good.

Key words: medical image segmentation, U-Net network, multi-scale and multi-dimensional feature fusion, skin lesion segmentation