Abstract: Due to the problems of blurred edges,excessive speckle noise,and low contrast in breast ultrasound images,an edge-guided multi-scale selective kernel U-Net(EMSK U-Net) method that fuses multiple features is proposed.The U-Net network has a symmetrical encoder-decoder structure,which can achieve better segmentation results on medical images with a small amount of data.EMSK U-Net adopts a network structure based on it,which combines dilated convolution with traditional convolution to form a selective kernel module,and applies it to the encoding path of the symmetric structure.Meanwhile,in the encoding part,EMSK U-Net performs the task of edge detection by extracting selective kernel features during down sampling.Through these methods,the spatial information of the image is enriched and the edge information of the image is refined,which effectively alleviates the difficult problem of segmentation caused by speckle noise and edge blur in breast ultrasound images,and the detection accuracy of small targets will also be improved to a certain extent.After that,in the decoding path of U-Net,EMSK U-Net obtains rich deep semantic information by building a multi-scale feature weighted aggregation module,realizes more information interaction between deep and shallow layers,and reduces the problem of low contrast.In general,EMSK U-Net jointly guides the segmentation of the network by complementing various information such as encoding part of the spatial information,edge information and decoding part of the depth feature information,so that the segmentation performance has been well improved.Experiments are conducted on three public breast ultrasound image datasets,and the results show that compared with other classical medical image segmentation methods and breast ultrasound segmentation methods,the EMSK U-Net algorithm performs well in various indicators.The performance of breast ultrasound image segmentation task has been significantly improved.

Key words: Breast ultrasound image segmentation, Feature fusion, Edge detection, Multi-scale features, Deep learning, U-Net