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题名:
Generalizing cell segmentation and quantification
作者: Wang ZZ(王振洲); Li HX(李海星)
作者部门: 机器人学研究室
通讯作者: 王振洲
关键词: Boundary filtering ; Noise blob filtering ; Threshold selection ; Calibration ; Iterative erosion
刊名: BMC BIOINFORMATICS
ISSN号: 1471-2105
出版日期: 2017
卷号: 18, 页码:1-16
收录类别: SCI
产权排序: 1
项目资助者: Chinese Academy of Sciences [Y5A1270101]
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.
语种: 英语
WOS记录号: WOS:000397509800005
WOS标题词: Science & Technology ; Life Sciences & Biomedicine
类目[WOS]: Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
关键词[WOS]: IMAGE SEGMENTATION ; MICROSCOPY ; IDENTIFICATION ; NANOPARTICLES ; BIOLOGY
研究领域[WOS]: Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/20293
Appears in Collections:机器人学研究室_期刊论文

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Recommended Citation:
Wang ZZ,Li HX. Generalizing cell segmentation and quantification[J]. BMC BIOINFORMATICS,2017,18:1-16.
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