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计算机系统应用英文版:2019,28(10):164-169
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基于U-Net的胸片肋骨影像抑制算法
(中国科学技术大学 信息科学技术学院, 合肥 230027)
Rib Suppression Algorithm Based on U-Net in Chest Radiographs
(School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China)
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Received:March 18, 2019    Revised:April 17, 2019
中文摘要: 由于X光图像只有二维信息,骨骼结构会和人体器官在图像中重叠,对医生和肺结节智能检测系统造成不利影响,抑制图像中的肋骨结构可以一定程度上改善上述情形.我们将肋骨视为图像中的噪声信息,使用图像去噪的方法来完成肋骨抑制的任务.本文采用深度卷积网络作为基础模型,分析并尝试多种策略来提升模型性能,最终我们采用Unet网络结构,通过跳跃连接以及残差学习策略增强网络细节表现能力.实验证明,我们的方法能够有效抑制肋骨结构在X光图片中的不利影响,对肺结节检测任务的性能有一定的提升.
Abstract:Skeletal structure and human organs overlap in the chest X-ray films, which has a negative impact on the intelligent detection system of doctors and pulmonary nodules, because X-ray image only has two-dimensional information. Restraining rib structure in the image can improve the above situation to a certain extent. We regard ribs as noise information in images, and use image denoising method to suppress ribs. In this study, we use deep convolution network as the basic model, and try to improve the performance of the model by analyzing and trying a variety of strategies. Ultimately, we use U-net network structure to enhance the performance of network details through jump connections and residual learning strategies. Experiments prove that the proposed method can effectively suppress the adverse effects of rib structure in X-ray images, and improve the performance of pulmonary nodule detection tasks.
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基金项目:合肥市借转补项目(YW201710120004)
引用文本:
焦庆磊,朱明,汪斌全,刘成林.基于U-Net的胸片肋骨影像抑制算法.计算机系统应用,2019,28(10):164-169
JIAO Qing-Lei,ZHU Ming,WANG Bin-Quan,LIU Cheng-Lin.Rib Suppression Algorithm Based on U-Net in Chest Radiographs.COMPUTER SYSTEMS APPLICATIONS,2019,28(10):164-169