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2017 ; 18
(1
): 189
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Generalizing cell segmentation and quantification
#MMPMID28335722
Wang Z
; Li H
BMC Bioinformatics
2017[Mar]; 18
(1
): 189
PMID28335722
show ga
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.
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