AI Article Synopsis
- Self-contained imaging systems, like the EVOS, are essential for live cell imaging in cell culture labs, allowing automated, programmable imaging of multi-well dishes.
- Initial attempts to analyze images from the EVOS faced several challenges, such as high background noise and focus inconsistencies, which complicated processing.
- A robust automated cell counting pipeline has been developed to effectively analyze these tiled images, improving accuracy and efficiency in cell quantification.
Article Abstract
Self-contained imaging systems are versatile instruments that are becoming a staple in cell culture laboratories. Many of these machines possess motorized stages and on-stage incubators that permit programmable imaging of live cells that make them a sensible tool for high-throughput applications. The EVOS imaging system is such a device and is capable of scanning multi-well dishes and stitching together multiple adjacent fields to produce coherent individual images of each well. Automated batch analysis and quantification of these tiled images does however require off-loading files to other software platforms. Our initial attempts to quantify tiled images captured on an EVOS device was plagued by some expected-and other unforeseeable-issues that arose at nearly every stage of analysis. These included: high background, illumination and stitching artifacts, low contrast, noise, focus inconsistencies, and image distortion-all of which negatively impacted processing efficiency. We have since overcome these obstacles and have created a rigorous cell counting pipeline for analyzing images captured by the EVOS scan function. We present development and optimization of this automated pipeline and submit it as an effective and facile tool for accurately counting cells from tiled images.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406032 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236397 | PLOS |
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