Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules

被引：13
|
作者：
Li, Xuewei [1 ]
Wang, Shuaijie [1 ]
Wei, Xi [2 ]
Zhu, Jialin [2 ]
Yu, Ruiguo [1 ]
Zhao, Mankun [1 ]
Yu, Mei [1 ]
Liu, Zhiqiang [1 ]
Liu, Shupei [1 ]
机构：
[1] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China
[2] Tianjin Med Univ, Canc Inst & Hosp, Tianjin, Peoples R China
来源：
IEEE 20TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS / IEEE 16TH INTERNATIONAL CONFERENCE ON SMART CITY / IEEE 4TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS) | 2018年
关键词：
Thyroid nodule; Ultrasound image processing; Convolutional neural network; Segmentation;
D O I：
10.1109/HPCC/SmartCity/DSS.2018.00147
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Ultrasound image segmentation plays an important role in judgement of benign and malignant thyroid nodules. Compared with the traditional convolutional neural network, the fully convolutional networks has better sparsity, higher precision and faster training speed. In this paper, we develop an 8-layer fully convolutional networks for ultrasound image segmentation of thyroid nodules, which is called FCN-Thyroid Nodules, or FCN-TN for short. We constructed a data set with 300 images to train FCN-TN. Each nodule is delineated by expert and served as ground truth for making comparison. The segmentation accuracy of 91% is obtained on the proposed network with 100 test images, which indicates that the fully convolutional networks has great potential in the field of ultrasound image segmentation of thyroid nodules.
引用
收藏
页码：886 / 890
页数：5
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