摘要
提出一种基于条件分割对抗网络(conditional segmentation adversarial network, cSegAN)的超声甲状腺结节分割模型。模型由分割器网络和判别器网络两个部分组成,其中分割器网络设计使用一种多扩张率卷积块联合对结节区域进行准确定位,通过学习提取结节深度和浅层特征信息,获得结节区域二值掩膜;判别器网络对比分割结果与金标准之间的差距对分割结果进行评估。经多次对抗训练,实验结果表明,本文所提模型像素精度达到0.953 1,优于其他分割模型,可以更加准确地实现超声甲状腺结节分割。
An ultrasonic thyroid nodule segmentation model based on conditional segmentation adversarial network(cSegAN) was proposed to achieve more accurate segmentation of thyroid nodules. The model is composed of two parts: a segmenter network and a discriminator network. The segmenter network design uses a multi-expansion rate convolution block to accurately locate the nodule area, learn to extract the depth and shallow feature information of the nodule, and obtain binary mask of nodule area;the discriminator network compares the gap between the segmentation result and the gold standard to evaluate the segmentation result. Through multiple adversarial training, the experimental results show that the pixel accuracy of the proposed model reaches 0.953 1, which is better than other segmentation models, and can achieve ultrasonic thyroid nodule segmentation more accurately.
作者
吴俊霞
强彦
王梦南
武仪佳
WU Junxia;QIANG Yan;WANG Mengnan;WU Yijia(College of Information and Computer,Taiyuan University of Technology,Jinzhong 030600,China)
出处
《太原理工大学学报》
CAS
北大核心
2023年第2期392-398,共7页
Journal of Taiyuan University of Technology
基金
国家自然科学基金资助项目(61872261)。
关键词
甲状腺结节分割
卷积神经网络
分割对抗网络
超声图像
对抗训练
thyroid nodule segmentation
convolutional neural network
segmentation adversarial network
ultrasound images
adversarial training
