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, CellProfiler and CellC and the precision of QC, ImageJ, 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.

Download full-text PDF

Source
http://dx.doi.org/10.2144/btn-2018-0072DOI Listing

Publication Analysis

Top Keywords

novel automated
8
cell detection
8
imagej cellprofiler
8
cellprofiler cellc
8
cell
7
quickcount novel
4
automated software
4
software rapid
4
rapid cell
4
detection quantification
4

Similar Publications

Introduction: Tumorous growths in the sellar region pose significant clinical challenges due to their proximity to critical visual structures such as the optic chiasm and optic nerves. Given their proximity to the optic system, these tumors are often diagnosed due to a progressive decrease in visual acuity. Thus, surgical intervention is crucial to prevent irreversible damage, as timely decompression can halt the progression of edema and subsequent optic atrophy.

View Article and Find Full Text PDF

Introduction: The extraction of DNA is the basis of molecular biology research. The quality of the extracted DNA is one of the key factors for the success of molecular biology experiments.

Objective: To select a suitable DNA extraction method for Chinese medicinal herbs and seeds.

View Article and Find Full Text PDF

Patient-derived organoids represent a novel platform to recapitulate the cancer cells in the patient tissue. While cancer heterogeneity has been extensively studied by a number of omics approaches, little is known about the spatiotemporal kinase activity dynamics. Here we applied a live imaging approach to organoids derived from 10 pancreatic ductal adenocarcinoma (PDAC) patients to comprehensively understand their heterogeneous growth potential and drug responses.

View Article and Find Full Text PDF

The fourth industrial revolution witnessed significant advancements in automating numerous aircraft inspection tasks. Still, certain critical procedures continue to rely on manual execution, including the aero-engine blade weighing process. This task is of paramount importance for blade mass inspection and engine dynamic balancing.

View Article and Find Full Text PDF

Exploring spiking neural networks for deep reinforcement learning in robotic tasks.

Sci Rep

December 2024

Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", Università di Bologna, 40126, Bologna, Italy.

Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving as the medium for asynchronous communication among neurons. Due to their inherent ability to capture input dynamics, SNNs hold great promise for deep networks in Reinforcement Learning (RL) tasks.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!