计算机科学
分割
人工智能
残余物
深度学习
卷积神经网络
像素
模式识别(心理学)
乳腺超声检查
计算机视觉
乳腺癌
乳腺摄影术
癌症
医学
算法
内科学
作者
Gongping Chen,Yu Dai,Jianxun Zhang
标识
DOI:10.1016/j.engappai.2022.105601
摘要
Breast ultrasound images segmentation is one of the key steps in clinical auxiliary diagnosis of breast cancer, which seriously threatens women’s health. Currently, deep learning methods have been successfully applied to breast tumors segmentation. However, blurred boundaries, heterostructure and other factors can cause serious missed detections and false detections in the segmentation results. In this paper, we developed a novel refinement residual convolutional network to segment breast tumors accurately from ultrasound images, which mainly composed of SegNet with deep supervision module, missed detection residual network and false detection residual network. In SegNet, we add six side-out deep supervision modules to guide the network to learn to predict precise segmentation masks scale-by-scale. In missed detection residual network, the receptive field provided by different dilation rates can provide more global information, which is easily lost in deep convolutional layer. The introduction of false detection and missed detection residual network can promotes the network to make more efforts on those hardly-predicted pixels to help us obtain more accurate segmentation results of the breast tumor. To evaluate the segmentation performance of the network, we compared with several state-of-the-art segmentation approaches using five quantitative metrics on two public breast datasets. Experimental results demonstrate that our method achieves the best segmentation results, which indicates that our method has better adaptability on breast tumors segmentation.
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