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Generalizing cell segmentation and quantification
Wang ZZ(王振洲); Li HX(李海星)
Department机器人学研究室
Source PublicationBMC BIOINFORMATICS
ISSN1471-2105
2017
Volume18Pages:1-16
Indexed BySCI
WOS IDWOS:000397509800005
Contribution Rank1
Funding OrganizationChinese Academy of Sciences [Y5A1270101]
KeywordBoundary Filtering Noise Blob Filtering Threshold Selection Calibration Iterative Erosion
AbstractBackground: 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.
Language英语
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS KeywordIMAGE SEGMENTATION ; MICROSCOPY ; IDENTIFICATION ; NANOPARTICLES ; BIOLOGY
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20293
Collection机器人学研究室
Corresponding AuthorWang ZZ(王振洲)
AffiliationState Key Laboratory for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
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.
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