I want to create something very basic for beginners for best practices of quality control of image analysis.
I googled a bit but could not find anything. Probably I missed many things?
Things that come to my mind are:
- For object segmentation:
- Save images with segmentation overlay
- Go through the segmentation overlay images and make notes like this:
- image_001, too many objects detected
- image_005, objects merged
- Go back to your image analysis pipeline and try to fix the algorithm for those images (consult an expert in your institute or www.image.sc if you need help).
- Run the analysis again and check whether it improved! check whether the changes did no make the results worse in other images!
- Quantification of object instance detection accuracy: TP, FP, FN, …
- Quantification of region segmentation accuracy: https://en.wikipedia.org/wiki/Jaccard_index
- If you have more than 100 images, going through all of them might be too much. Then you need an efficient way to only look at “outliers”, such as something like this: https://github.com/hmbotelho/shinyHTM
a.s.o., this is not meant to be comprehensive, but just to indicate the level I would like to target.
I am super thankful for any hints on this subject!