Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal

被引:27
作者
Khan, Shujaat [1 ]
Huh, Jaeyoung [1 ]
Ye, Jong Chul [1 ]
机构
[1] Korea Adv Inst Sci & Technol KAIST, Dept Bio & Brain Engn, Daejeon 34141, South Korea
基金
新加坡国家研究基金会;
关键词
Ultrasonic imaging; Imaging; Unsupervised learning; Generators; Deep learning; Acoustics; Training; Optimal transport; ultrasound (US) artifact removal; unsupervised deep learning; DECONVOLUTION; FIELDS;
D O I
10.1109/TUFFC.2021.3056197
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications.
引用
收藏
页码:2086 / 2100
页数:15
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