Deep autoencoder features for registration of histology images

Abstract

Registration of histology whole slide images of consecutive sections of a tissue block is mandatory for cross-slide analysis. Due to the stain variations, a feature-based method for deriving the transformation maps for these images is considered to be a reasonable choice as compared to the methods which work on image intensities. Autoencoders have been employed in a wide variety of applications due to their potential for representation learning and transfer learning for deep architectures. Representation learned by autoencoders has been used for a number of challenging problems including classification and regression. In this study, we analyze deep autoencoder features for the purpose of registering histology images by maximizing the feature similarities between the fixed and moving images. In this paper, we demonstrate the capability of autoencoder features for registration of histology images.