Weakly supervised semantic segmentation of Crohn's disease tissues from abdominal MRI

We address the problem of weakly supervised segmentation (WSS) of medical images which is more challenging and has potentially greater applications in the medical imaging community. Training images are labeled only by the classes they contain, and not by the pixel labels. We make use of the Multi Image Model (MIM) for weakly supervised segmentation which exploits superpixel features and assigns labels to every pixel. MIM connects superpixels from all training images in a data driven fashion. Test images are integrated into the MIM for predicting their labels, thus making full use of the training samples. Experimental results on abdominal magnetic resonance (MR) images of patients with Crohn's disease show that WSS performs close to fully supervised methods and given sufficient samples can perform on par with fully supervised methods.