摘要
针对乳腺超声图像边缘模糊、斑点噪声多、对比度低等问题,提出了一种融合多特征的边缘引导多尺度选择性核U-Net(Edge-guided Multi-scale Selective Kernel U-Net,EMSK U-Net)方法。EMSK U-Net采用基于U-Net的对称编解码结构可以适应小数据集医学图像分割的特点,将扩张卷积与传统卷积构成选择性核模块作用于编码路径,并提取下采样过程中的选择性核特征进行边缘检测任务,在丰富图像空间信息的同时细化边缘信息,有效缓解斑点噪声和边缘模糊的问题,在一定程度上可以提升小目标的检测精度。然后在解码路径通过多尺度特征加权聚合获取丰富的深层语义信息,多种信息之间相互补充,从而提升网络的分割性能。在3个公开的乳腺超声图像数据集上的实验结果表明,与其他分割方法相比,EMSK U-Net算法各项指标表现良好,分割性能有显著提升。
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.
作者
白雪飞
马亚楠
王文剑
BAI Xuefei;MA Yanan;WANG Wenjian(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing,Ministry of Education(Shanxi University),Taiyuan 030006,China)
出处
《计算机科学》
CSCD
北大核心
2023年第3期199-207,共9页
Computer Science
基金
国家自然科学基金(61703252,U21A20513,62076154,62276161)
山西省重点研发项目(202102150401013)
山西省研究生教育创新项目(2022Y145)。
