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深度学习在超声检查中的技术趋势与应用：图像质量增强、诊断支持及工作流程效率提升

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency.

作者信息

Yi Jonghyon, Kang Ho Kyung, Kwon Jae-Hyun, Kim Kang-Sik, Park Moon Ho, Seong Yeong Kyeong, Kim Dong Woo, Ahn Byungeun, Ha Kilsu, Lee Jinyong, Hah Zaegyoo, Bang Won-Chul

机构信息

Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea.

DR Imaging R&D Lab, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea.

出版信息

Ultrasonography. 2021 Jan;40(1):7-22. doi: 10.14366/usg.20102. Epub 2020 Sep 14.

DOI:10.14366/usg.20102
PMID:33152846
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7758107/
Abstract

In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification, detection, segmentation, and generation. Ultrasonography applications for image processing and diagnosis are then reviewed and summarized, along with some representative imaging studies of the breast, thyroid, heart, kidney, liver, and fetal head. Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.

摘要

在本次对深度学习在超声成像领域最新应用的综述中，简要介绍了深度学习网络的架构，以用于分类、检测、分割和生成等医学成像应用。随后对超声成像在图像处理和诊断方面的应用进行了综述和总结，并列举了一些关于乳腺、甲状腺、心脏、肾脏、肝脏和胎儿头部的代表性成像研究。还综述了在增强工作流程方面所做的努力，重点关注视图识别、扫描引导、图像质量评估以及量化和测量。最后，针对图像质量提升、诊断支持以及工作流程效率的改进提出了一些未来展望，并阐述了其中存在的障碍、益处以及必要的合作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b24/7758107/f61e467765f6/usg-20102f10.jpg
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