Deep learning in ultrasound elastography imaging: A review.
Deep learning in ultrasound elastography imaging: A review.
复制标题
超声弹性成像中的深度学习：综述。
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
G. Cloutier
中科院分区：
文献类型：
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作者：
Hongliang Li;M. Bhatt;Zhen Qu;Shiming Zhang;Martin C. Hartel;A. Khademhosseini;G. Cloutier
已知组织的机械性质的变化与某些疾病的发生和进展相关。超声弹性成像是一种使用超声成像来表征组织刚度的技术，其通过使用准静态弹性成像或自然器官脉动弹性成像来测量组织应变，或者通过使用动态弹性成像来追踪由源或自然振动引起的传播剪切波。近年来，深度学习开始在超声弹性成像研究中崭露头角。在这篇综述中，描述了计算机视觉社区中几种常见的深度学习框架，如多层感知器，卷积神经网络和递归神经网络。然后，在算法开发和临床诊断方面重新审视了使用这种深度学习技术的超声弹性成像的最新进展。最后，对超声弹性成像中深度学习的当前挑战和未来发展进行了展望。本文受版权保护。All rights reserved.
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.