Last I checked Roboflow only had point-to-point vector masks for segmentation. In my experience that makes getting quality annotations a pain. In Labelbox, you can also hold in the mouse button. Hasty.ai focus on auto annotations, and by the look of the image you posted, it might be a good fit for your usecase.

That size would do well as a PoC, not much more, and you should be able to annotate all the data within a day or two. Automation does not make that much sense at this scale. I love Roboflow for bounding boxes, but LabelBox has superior tools for segmentation. Sure, with this small data set you can use cross validation, although a hold out test set is also preferable. I would almost consider hand-picking the test set at this scale to make sure you get a sense of how it performa on challenging examples. What is the pixel size of your images? I know microscopy/histology images typically can cover large areas and one image could in fact be considered a mosaic of many “normal” sized images.

I would use instance segmentation, it will feed the network more information and increase the chance if success. The output is also easier to interpret to guide data selection in the next iteration. The annotation process is more labour intensive, but using good tools/annotation platform go a long way to speed things up. Once your model is good enough, it is mostly a matter of correcting small mistakes.

Put it to test with real data. Putting a lot of effort into tuning a model on a fixed data set that will eventually be deployed is a waste of time. And dont freak out when it fails! Just add more quality data from when it is deployed.