Visual saliency based active learning for prostate MRI segmentation

Abstract

We propose an active learning (AL) approach for prostate segmentation from magnetic resonance (MR) images. Our label query strategy is inspired from the principles of visual saliency that has similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low level features. Random walks identify the most informative node which is equivalent to the label query sample in AL. Experimental results on the MICCAI 2012 Prostate segmentation challenge show the superior performance of our approach to conventional methods using fully supervised learning.