Image Analysis Quality Control
Part 2: Tissue Section QC
Getting the image quality right before embarking on advanced image analysis may be an obvious statement to make. However, there are multiple ways to assess image quality and this speaks to the need for robust quality control (QC) processes throughout an image analysis workflow. In the second part of our 3-part Image Analysis Quality Control Blog series, we look at a number of potential issues identified during Tissue Section QC and the impact they can have on image analysis data if not remedied.
There are many factors to consider when deciding if an image is suitable for image analysis and these are just some to include when conducting Tissue Section QC. To find out what else we include in our QC check, click here to read Image Analysis Quality Control Part 1: Image Scan QC and look out for part 3 of this QC Blog series which will cover Staining QC.
Encountering some of the issues above does not necessarily mean that an image cannot be analysed as some problems can be overcome, for example by negative annotation or additional algorithm development. Unfortunately, at OracleBio we do have to fail a significant number of images every year because the image analysis data generated will not be robust. If you are unsure if your images are suitable for quantitative analysis or need advice on how to avoid encountering some of these issues, please contact us.