Something I've had a lot of success with (in cases where you're automating the same task with the same lighting) is having a human operator manually choose a variety of in-sample and out-of-sample regions, ideally with some of those being near real boundaries. Then train a (very simple -- details matter, but not a ton) local model to operate on small image patches and output probabilities for each pixel.

One fun thing is that with a simple model it's not much slower than techniques like otsu (you're still doing a roughly constant amount of vectorized, fast math for each pixel), but you can grab an alpha channel for free even when working in colored spaces, allowing you to near-perfectly segment the background out from an image.

The UX is also dead-simple. If a human operator doesn't like the results, they just click around the image to refine the segmentation. They can then apply directly to a batch of images, or if each image might need some refinement then there are straightforward solutions for allowing most of the learned information to transfer from one image to the next, requiring much less operator input for the rest of the batch.

As an added plus, it also works well even for gridlines and other stranger backgrounds, still without needing any fancy algorithms.