More of a conceptual question here:
I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting out, say, ligaments, from a medical image. If I train that model on images that contain just a single labelled ligament, it will be able to segment out single ligaments pretty well, I assume. If I present it with an image with multiple ligaments, should it also be able to segment the multiple ligaments well too?
Based on my understanding, semantic segmentation is just pixel-wise classification. As a result, shouldn't the number of the objects in the image not be relevant since it's only looking at individual pixels? So as long as a pixel matches that of a ligament, it should be able to segment it equally right?
Or am I misunderstanding some piece?
Basically, if I train a U-Net on images with just single ligaments, will it also be able to segment images with multiple ligaments equally as well based on my logic above?