I am doing an image segmentation project for mineral petrographic thin sections. The shape and size of the mineral are important, so precise segmentation is required.
I annotated 40 images and used to train a UNet model to predict the classification of each pixel (class A, class B, holes, touched boundaries), and then use Watershed for post-processing, but the results are not satisfactory (only some of images are segmented right) .
The mineral grains in my pictures are in contact with each other, and the size, color, and texture are very different. I think this is the main reason for the difficulty of segmentation.
Please help me, is there any relevant research or successful segmentation method (whether it is deep learning or traditional methods) on this kind of images?
Or a simpler annotation tool is welcome to suggest, I really don’t want to use PhotoShop for annotating.
Sement result of above image
Sement result of other picture1
Sement result of other picture2