Open AccessQuickCount®: a novel automated software for rapid cell detection and quantification
Author(s) -
Kai Hung Tiong,
Jit Chang,
Dharini Pathmanathan,
Muhammad Zaki Hidayatullah Fadlullah,
Pei San Yee,
Chee Sun Liew,
Zainal Ariff Abdul Rahman,
Kheng Ling Beh,
Sok Ching Cheong
Publication year - 2018
Publication title -
biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/btn-2018-0072
Subject(s) - intraclass correlation , software , cell counting , computer science , precision and recall , plot (graphics) , recall , artificial intelligence , cell , statistics , reproducibility , biology , mathematics , genetics , linguistics , philosophy , cell cycle , programming language
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 ImageJ auto , CellProfiler and CellC and the precision of QC, ImageJ auto , 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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom