DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image

doi:10.1364/BOE.431798

	DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image
	Gao, Shan 1,2; Xu, Fan 3; Li, Hongjia 1,2; Xue, Fudong 4; Zhang, Mingshu 4; Xu, Pingyong 2,4; Zhang, Fa 1
	2021-09-01
发表期刊	BIOMEDICAL OPTICS EXPRESS
ISSN	2156-7085
卷号	12 期号:9 页码:5751-5769
摘要	Super-resolution fluorescence microscopy, with a spatial resolution beyond the diffraction limit of light, has become an indispensable tool to observe subcellular structures at a nanoscale level. To verify that the super-resolution images reflect the underlying structures of samples, the development of robust and reliable artifact detection methods has received widespread attention. However, the existing artifact detection methods are prone to report false alert artifacts because it relies on absolute intensity mismatch between the wide-field image and resolution rescaled super-resolution image. To solve this problem, we proposed DETECTOR, a structural information-guided artifact detection method for super-resolution images. It detects artifacts by computing the structural dissimilarity between the wide-field image and the resolution rescaled super-resolution image. To focus on structural similarity, we introduce a weight mask to weaken the influence of strong autofluorescence background and proposed a structural similarity index for super-resolution images, named MASK-SSIM. Simulations and experimental results demonstrated that compared with the state-of-the-art methods, DETECTOR has advantages in detecting structural artifacts in super-resolution images. It is especially suitable for wide-field images with strong autofluorescence background and super-resolution images of single molecule localization microscopy (SMLM). DETECTOR has extreme sensitivity to the weak signal region. Moreover, DETECTOR can guide data collection and parameter tuning during image reconstruction. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
DOI	10.1364/BOE.431798
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0504702] ; National Key Research and Development Program of China[2017YFA0505300] ; National Natural Science Foundation of China[21778069] ; National Natural Science Foundation of China[21927813] ; National Natural Science Foundation of China[31870857] ; National Natural Science Foundation of China[32027901] ; National Natural Science Foundation of China[61932018] ; National Natural Science Foundation of China[62072441] ; Beijing Municipal Natural Science Foundation[L182053] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB37040301]
WOS研究方向	Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Biochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000693319200005
出版者	OPTICAL SOC AMER
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17146
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Mingshu
作者单位	1.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Purdue Univ, Weldon Sch Biomed Engn, W Lafayette, IN 47907 USA 4.Chinese Acad Sci, Inst Biophys, Key Lab RNA Biol, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Gao, Shan,Xu, Fan,Li, Hongjia,et al. DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image[J]. BIOMEDICAL OPTICS EXPRESS,2021,12(9):5751-5769.
APA	Gao, Shan.,Xu, Fan.,Li, Hongjia.,Xue, Fudong.,Zhang, Mingshu.,...&Zhang, Fa.(2021).DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image.BIOMEDICAL OPTICS EXPRESS,12(9),5751-5769.
MLA	Gao, Shan,et al."DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image".BIOMEDICAL OPTICS EXPRESS 12.9(2021):5751-5769.