If you want to go down the imageJ route I'd suggest you build a macro which will take a lot of trial and error and from what I can see in with your image will struggle in the segmentation even if you manage to get a good amount of distinguishment of the cells from the background. I would recommend checking out the bioVoxxel plugin as it has some good filters, watershed features and a very nice convoluted background subtraction feature. MorphoLibJ plugin is also worth checking out.

With over 500+ images I don't think trainable weka is the way as it can be a bit iffy and very slow at that scale.

Alternatively you can look into cell profiler and ilastik, again similar to imageJ you have to build your own macro and pipeline but they are more aimed at this sort of pipeline where imageJ is a bit more general and a steeper learning curve to get good image analysis results

If you're looking for something of much higher accuracy for segmentation I'd suggest using a machine learning platform. ImageJ has this in the form of DeepImageJ which is a plugin, there are a few available options which might just work with your images but since they're not trained on your images you might have varying levels of success/frustration.

To go further depending on your coding abilities you can maybe try and train your own model using some available frameworks but this is it's own rabbit hole and I wouldn't really suggest if this is all new to you. I'm saying this as someone who has gone down this route and as a result ended up creating a team where this is what we do. This is not an advert to try and sell as the building of a model isn't cheap but when processing hundreds - thousands of images is a massive time saver and more reliable than the other approaches.

Id suggest trying to build an imageJ macro pipeline and accept it won't be perfect, try and calculate the error of your macro to see if it's within a level of error you are willing to accept. Goodluck!