Multi-Stage U-Net Automatic Segmentation of Thyroid Ultrasound Images

被引：0

作者：

Wang, Bo ^{[1
,3
]}

Yuan, Fengqiang ^{[2
]}

Chen, Zongren ^{[1
]}

Hu, Jianhua ^{[1
]}

Yang, Jiahui ^{[1
]}

Liu, Xia ^{[2
,3
]}

机构：

[1] Computer Engineering Technical College(Artificial Intelligence College), Guangdong Polytechnic of Science and Technology, Guangdong, Zhuhai

[2] School of Automation, Harbin University of Science and Technology, Harbin

[3] Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin

来源：

Computer Engineering and Applications | 2024年 / 59卷 / 05期

关键词：

image segmentation; multi-stage U-Net; thyroid ultrasound images;

D O I：

10.3778/j.issn.1002-8331.2110-0357

中图分类号：

学科分类号：

摘要：

Thyroid ultrasonography is widely used in the diagnosis of thyroid diseases. To solve the problems of low contrast, blurred edges and serious speckle noise in thyroid ultrasound images, a deep convolutional network model based on multi-stage U-Net is proposed to achieve automatic segmentation of thyroid glands and thyroid nodules. Using U-Net as the basic network framework, this model realizes the deep information extraction of image edge through continuously advanced feature fusion. Meanwhile, a multi-scale residual convolution module is used in the model to further improve the segmentation accuracy. The comparative experimental results show that this model can obtain better segmentation results compared with other methods, which has certain clinical application value. © 2024 Moscow Polytechnic University. All rights reserved.

引用

页码：205 / 212

页数：7

共 26 条

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