biological fluorescence supervised machine learing
Whether to quantify things at all with depend on the technique you want to use. An end-to-end CNN (convolutional neural network) for example would just be fed the 3D image for training and predictions. However the major downside to CNNs is that you need a LOT of data to train them, though perhaps you can get lucky and find an existing CNN that you can use and just retrain the last couple layers.

Other than that, you're going to need to come up with as many (hopefully useful) metrics as you can. There are then a lot of techniques one could try, ranging from simple things like regression to random forests to SVMs, among many others. In general I think simple regression and random forests are the easiest to get started with.