@inproceedings{09188551e325434faac39df18504cd3d,
title = "Detection and monitoring of microwave ablation by ultrasound imaging based on convolutional neural network",
abstract = "Microwave ablation (MWA) is widely used in clinical treatment, but conventional ultrasound B-mode monitoring imaging method cannot provide real-time changes in the ablation area accurately during thermal ablation. With the further maturity of classification technology of convolutional neural network (CNN), the CNN will certainly play an important role in the processing and analysis of medical images. In this study, we proposed and evaluated an US image based on CNN architecture for the detection and monitoring of thermal lesions induced by MWA in porcine liver. The values of the ablation area under the receiver operating characteristic curve for US image based on CNN and B-mode image were 0.8728 and 0.6904, respectively. The results show that it is feasible to use convolutional neural network to monitor the changes of ablation area during MWA.",
keywords = "Convolutional Neural Network (CNN), Microwave ablation, Monitoring, Ultrasound imaging",
author = "Mengke Wang and Shan Wu and Xin Jia and Shaoqiang Shang and Tianqi Xu and Dapeng Li and Mingxi Wan and Siyuan Zhang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Ultrasonics Symposium, IUS 2020 ; Conference date: 07-09-2020 Through 11-09-2020",
year = "2020",
month = sep,
day = "7",
doi = "10.1109/IUS46767.2020.9251480",
language = "英语",
series = "IEEE International Ultrasonics Symposium, IUS",
publisher = "IEEE Computer Society",
booktitle = "IUS 2020 - International Ultrasonics Symposium, Proceedings",
}