鉴别器
分割
计算机科学
人工智能
领域(数学分析)
模式识别(心理学)
图像分割
域适应
GSM演进的增强数据速率
水准点(测量)
计算机视觉
深度学习
图像(数学)
适应(眼睛)
样品(材料)
数学
地理
光学
物理
数学分析
分类器(UML)
探测器
电信
化学
色谱法
大地测量学
作者
Guozheng Sui,Xuefeng Liu,Shuang Chen,Shunli Liu,Zaixian Zhang
标识
DOI:10.1088/1361-6560/ace498
摘要
Abstract With the development of deep learning, the methods based on transfer learning have promoted the progress of medical image segmentation. However, the domain shift and complex background information of medical images limit the further improvement of the segmentation accuracy. Domain adaptation can compensate for the sample shortage by learning important information from a similar source dataset. Therefore, a segmentation method based on adversarial domain adaptation with background mask (ADAB) is proposed in this paper. Firstly, two ADAB networks are built for the source and target data segmentation, respectively. Next, to extract the foreground features that are the input of the discriminators, the background masks are generated according to the region growth algorithm. Then, to update the parameters in the target network without being affected by the conflict between the distinguishing differences of the discriminator and the domain shift reduction of the adversarial domain adaptation, a gradient reversal layer propagation is embedded in the ADAB model for the target data. Finally, an enhanced boundaries loss is deduced to make the target network sensitive to the edge of the area to be segmented. The performance of the proposed method is evaluated in the segmentation of pulmonary nodules in computed tomography images. Experimental results show that the proposed approach has a potential prospect in medical image processing.
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