I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in the middle of the picture and an outer object, which is ring-shaped, and contains the round object. This gap is "thin" and is only a small proportion of the whole image. In my problem having a small a gap is good, since then the two objects have a good connection to each other.

My questions:

  1. Is it possible to feed such large images on a CNN? Downscaling the images seems a bad idea since the gap is thin and most of the relevant information will be lost. From what I've seen CNN are trained on much smaller images.

  2. Since the problem is symmetric in some sense is it a good idea to split the image in 4 (or more) smaller images?

  3. Are CNNs able to detect such small regions in such a huge image? From what I've seen in the literature, mostly larger objects are segmented such as organs etc.

I would appreciate some ideas and help. It is my first post on the site so hopefully I didn't make any mistakes.



Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.