[1]王尧,黄钢.深度学习在细胞图像分析中的应用进展[J].中国医学物理学杂志,2022,39(8):963-966.[doi:DOI:10.3969/j.issn.1005-202X.2022.08.008]
 WANG Yao,HUANG Gang,Advances in application of deep learning in cell image analysis[J].Chinese Journal of Medical Physics,2022,39(8):963-966.[doi:DOI:10.3969/j.issn.1005-202X.2022.08.008]
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深度学习在细胞图像分析中的应用进展()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第8期
页码:
963-966
栏目:
医学影像物理
出版日期:
2022-08-04

文章信息/Info

Title:
Advances in application of deep learning in cell image analysis
文章编号:
1005-202X(2022)08-0963-04
作者:
王尧1黄钢12
1.上海理工大学健康科学与工程学院, 上海 200093; 2.上海健康医学院附属嘉定中心医院上海市分子影像学重点实验室, 上海 201318
Author(s):
WANG Yao1 HUANG Gang1 2
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Key Laboratory of Molecular Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
关键词:
细胞图像深度学习图像处理综述
Keywords:
Keywords: cell image?eep learning?mage processing review
分类号:
R318;TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2022.08.008
文献标志码:
A
摘要:
随着显微技术的不断发展,显微镜已克服人眼的局限性成为研究细胞生物学必不可少的工具。近年来,显微技术在速度、规模以及分辨率等方面都有了巨大的进步。深度学习在图像处理尤其是细胞图像处理中的应用受到广泛关注。本文针对深度学习在细胞图像分析中常用的算法进行介绍,并对近几年深度学习在细胞图像处理中的应用进行详细论述,包括图像分类、图像分割、目标跟踪、图像超分辨重建4个方面。最后展望了深度学习在细胞图像分析中的机遇和挑战。
Abstract:
Abstract: With the development of microscopy technology, microscope has overcome the limitations of human eyes and became an essential tool for the study of cell biology, and it has made tremendous achievements in speed, scale and resolution in recent years.?n addition, the application of deep learning in image processing, especially in cell image processing, has attracted extensive attention.?erein the commonly used algorithms of deep learning in cell image analysis are introduced, and the application of deep learning in cell image processing in recent years is discussed in detail, including image classification, image segmentation, target tracking, and image super-resolution reconstruction.?inally, the opportunities and challenges of deep learning in cell image analysis are forecasted.

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备注/Memo

备注/Memo:
【收稿日期】2022-02-15 【基金项目】国家自然科学基金(82127807);上海市分子影像学重点实验室建设项目(18DZ2260400) 【作者简介】王尧,硕士,研究方向:细胞图像处理,E-mail: 1805336701@qq.com 【通信作者】黄钢,教授,博士生导师,研究方向:核医学,E-mail: huanggang@sumhs.edu.cn
更新日期/Last Update: 2022-09-05