纹理(宇宙学)
对比度(视觉)
人工智能
计算机科学
块(置换群论)
解码方法
超声波
计算机视觉
冲程(发动机)
模式识别(心理学)
图像纹理
频道(广播)
医学
放射科
图像(数学)
图像处理
数学
工程类
电信
机械工程
几何学
作者
Wenjun Zhou,Tianfei Wang,Yuhang He,Shuyun Xie,Aijing Luo,Bo Peng,Lixue Yin
出处
期刊:Mathematical Biosciences and Engineering
[American Institute of Mathematical Sciences]
日期:2023-01-01
卷期号:20 (9): 15623-15640
摘要
Ischemic heart disease or stroke caused by the rupture or dislodgement of a carotid plaque poses a huge risk to human health. To obtain accurate information on the carotid plaque characteristics of patients and to assist clinicians in the determination and identification of atherosclerotic areas, which is one significant foundation work. Existing work in this field has not deliberately extracted texture information of carotid from the ultrasound images. However, texture information is a very important part of carotid ultrasound images. To make full use of the texture information in carotid ultrasound images, a novel network based on U-Net called Contrast U-Net is designed in this paper. First, the proposed network mainly relies on a contrast block to extract accurate texture information. Moreover, to make the network better learn the texture information of each channel, the squeeze-and-excitation block is introduced to assist in the jump connection from encoding to decoding. Experimental results from intravascular ultrasound image datasets show that the proposed network can achieve superior performance compared with other popular models in carotid plaque detection.
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