QuickCount®: a novel automated software for rapid cell detection and quantification. (27th November 2018)

• Record Type: Journal Article
• Authors: Tiong, Kai Hung; Chang, Jit Kang; Pathmanathan, Dharini; Hidayatullah Fadlullah, Muhammad Zaki; Yee, Pei San; Liew, Chee Sun; Abdul Rahman, Zainal Ariff; Beh, Kheng Ling; Cheong, Sok Ching
• Is Part Of: Biotechniques. Volume 65:Number 6(2018)
• Page Start: 322
• Page End: 330
• Publication Date: 2018-11-27
• Subjects: accurate -- automation -- cell count -- cell quantification -- scalable -- time-efficient -- user-friendly; Biology, Experimental -- Periodicals; Molecular biology -- Periodicals; Medical technology -- Periodicals; Biology, Experimental; Medical technology; Molecular biology; Clinical Laboratory Techniques -- Periodicals; Research -- Periodicals; Medical Laboratory Science -- Periodicals; Periodicals; Electronic journals; 570
• DOI: 10.2144/btn-2018-0072
• Languages: English
• ISSNs: 0736-6205

Abstract

We describe a novel automated cell detection and counting software, QuickCount ® (QC), designed for rapid quantification of cells. The Bland–Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.