Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life

Abstract

Quantify dynamic structural changes in the first year of life is a key step in early brain development studies, which is indispensable to accurate deformable image registration. However, very few registration methods can work universally well for infant brain images at arbitrary development stages from birth to one year old, mainly due to (1) large anatomical variations and (2) dynamic appearance changes. In this paper, we propose a novel learning-based registration method to not only align the anatomical structures but also estimate the appearance difference between two infant MR images with possible large age gap. To achieve this goal, we leverage the random forest regression and auto-context model to learn the evolution of shape and appearance from a set of longitudinal infant images (with subject-specific temporal correspondences well determined) and then predict both the deformation pathway and appearance change between two new infant subjects. After that, it becomes much easier to deploy any conventional image registration method to complete the remaining registration since the above challenges for current state-of-the-art registration methods have been solved successfully. We apply our proposed registration method to align infant brain images of different subjects from 2-week-old to 12-month-old. Promising registration results have been achieved in terms of registration accuracy, compared to the counterpart registration methods.