Deep learning-based color transfer biomedical imaging technology

被引：0

作者：

Bian Y. ^{[1
]}

Xing T. ^{[1
]}

Deng W. ^{[2
]}

Xian Q. ^{[3
]}

Qiao H. ^{[3
]}

Yu Q. ^{[4
]}

Peng J. ^{[4
]}

Yang X. ^{[5
]}

Jiang Y. ^{[6
]}

Wang J. ^{[7
]}

Yang S. ^{[7
]}

Shen R. ^{[6
]}

Shen H. ^{[1
]}

Kuang C. ^{[8
]}

机构：

[1] School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing

[2] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun

[3] Chongqing Jialing Huaguang Optoelectronic Technology Co. LTD, Chongqing

[4] Beijing Environmental Satellite Engineering Institute, Beijing

[5] School of Optoelectronic Science and Engineering, Soochow University, Suzhou

[6] Department of General Surgery, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou

[7] Center of Reproduction and Genetics, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou

[8] College of Optical Science and Engineering, Zhejiang University, Hangzhou

来源：

Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2022年 / 51卷 / 02期

关键词：

Artificial intelligence; Biomedical imaging; Color transfer; Deep learning;

D O I：

10.3788/IRLA20210891

中图分类号：

学科分类号：

摘要：

In traditional pathology detection, the speed of diagnosis is limited due to the complex staining process and single observation form. The staining process is essentially associating color information with morphological features, and the effect is equivalent to that of biomedical images of modern digital technology. Sense segmentation, which allows researchers to greatly reduce the steps of biomedical imaging processing samples through computational post-processing, and achieve imaging results consistent with the gold standard of traditional medical staining. In recent years, the development of artificial intelligence deep learning has contributed to the effective combination of computer-aided analysis and clinical medicine, and artificial intelligence color transfer technology has gradually shown high development potential in biomedical imaging analysis. This paper will review the technical principles of deep learning color transfer, enumerate some applications of such technologies in the field of biomedical imaging, and look forward to the research status and possible development trends of artificial intelligence color transfer in the field of biomedical imaging. Copyright ©2022 Infrared and Laser Engineering. All rights reserved.

引用

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