Generalizing cell segmentation and quantification

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Generalizing cell segmentation and quantification
	Wang ZZ(王振洲); Li HX(李海星)
刊名	BMC BIOINFORMATICS
	2017
卷号	18 页码:1-16
关键词	Boundary filtering Noise blob filtering Threshold selection Calibration Iterative erosion
ISSN号	1471-2105
通讯作者	王振洲
产权排序	1
中文摘要	Background: In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work. Results: To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods. Conclusions: The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
类目[WOS]	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
研究领域[WOS]	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
关键词[WOS]	IMAGE SEGMENTATION ; MICROSCOPY ; IDENTIFICATION ; NANOPARTICLES ; BIOLOGY
收录类别	SCI
语种	英语
WOS记录号	WOS:000397509800005
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/20293]
专题	沈阳自动化研究所_机器人学研究室
推荐引用方式 GB/T 7714	Wang ZZ,Li HX. Generalizing cell segmentation and quantification[J]. BMC BIOINFORMATICS,2017,18:1-16.
APA	Wang ZZ,&Li HX.(2017).Generalizing cell segmentation and quantification.BMC BIOINFORMATICS,18,1-16.
MLA	Wang ZZ,et al."Generalizing cell segmentation and quantification".BMC BIOINFORMATICS 18(2017):1-16.