期刊
BIOTECHNIQUES
卷 66, 期 4, 页码 179-185出版社
FUTURE SCI LTD
DOI: 10.2144/btn-2018-0163
关键词
cell count; Chagas disease; image analysis; machine learning; microscopy; pattern recognition; protozoan parasite; Trypanosoma cruzi
资金
- KAKENHI [18K15141]
- Grants-in-Aid for Scientific Research [18K15141] Funding Source: KAKEN
Automated cell counters that utilize still images of sample cells are widely used. However, they are not well suited to counting slender, aggregate-prone microorganisms such as Trypanosoma cruzi. Here, we developed a motion-based cell-counting system, using an image-recognition method based on a cubic higher-order local auto-correlation feature. The software successfully estimated the cell density of dispersed, aggregated, as well as fluorescent parasites by motion pattern recognition. Loss of parasites activeness due to drug treatment could also be detected as a reduction in apparent cell count, which potentially increases the sensitivity of drug screening assays. Moreover, the motion-based approach enabled estimation of the number of parasites in a co-culture with host mammalian cells, by disregarding the presence of the host cells as a static background.
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