Mammography Registration for Unsupervised Learning Based on CC and MLO Views

Abstract

Mammography image usually contains two views in different orientations---Cranial Caudal (CC) and Mediolateral Oblique (MLO). In clinical decision making, the location of the lesions on the CC and MLO views are usually different. And the shape of breast varies greatly among patients, therefore, two views are necessary for evaluating the information in a comprehensively manner. In this paper, we propose an unsupervised registration algorithm based on CC and MLO views of mammography, which learns the deformation function through a Convolutional Neural Network (CNN). This function maps the input image to the corresponding deformation field and generates an image with the same shape as the template image after deformation, so that the doctor can better observe the two views. According to the radiologist's assessment, our work can contribute to medical image analysis and processing while providing novel guidance in learning-based registration and its applications.