Speckle reduction of medical ultrasound images using deep learning with fully convolutional network

被引:12
|
作者
Ando, Kazuma [1 ]
Nagaoka, Ryo [2 ]
Hasegawa, Hideyuki [2 ]
机构
[1] Univ Toyama, Grad Sch Sci & Engn, Toyama 9308555, Japan
[2] Univ Toyama, Acad Assembly, Fac Engn, Toyama 9308555, Japan
关键词
D O I
10.35848/1347-4065/ab80a5
中图分类号
O59 [应用物理学];
学科分类号
摘要
Smoothing filters are frequently used for speckle reduction of medical ultrasound images. However, such filters may cause loss of the detailed structures of tissues in terms of image contrast. To improve image contrast in speckle reduction, we investigated a filter for medical ultrasound images using deep learning with a fully convolutional network, which was trained with pairs of input and target data generated by computer simulation. The proposed method achieved higher contrast-to-noise ratio and contrast values than the conventional methods with about 300 times faster processing speed than the NL-means filter. (C) 2020 The Japan Society of Applied Physics
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页数:4
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