Dear image.sc community,
I am currently looking into measures to quantify segmentation quality to systematically compare segmentation approaches.
General approaches I identified from the literature are:
Use fully labeled ground truth masks and match masks with ground truth masks using the intersect over union criterion. Then calculate F1 score, false positives, missing, merges, splits…
Eg used here: https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23863
Using manual classification of objects as under- over and well segmented.
-> Can be combined with supervised learning to build a classifier to do this more automatized
Other criteria such as size distribution, expected number of cell objects, …
Are there some other important measures I missed? Could you recommend some good papers either reviewing/establishing or simply using other ways to evaluate segmentation quality?
I would be particularly interested in measures that do not require fully labeled ground truth, as this seems rather difficult/unreliable to get in the images I am working with.
Thanks already for any hints!