Full anatomical labeling of magnetic resonance images of human brain by registration with multiple atlases

Abstract

The problem of automatic segmentation of magnetic resonance (MR) images of human brain into anatomical structures is considered. Currently, the most popular segmentation algorithms are based on the registration (matching) of the input image with (to) an atlas—an image for which an expert labeling is known. Segmentation on the basis of registration with multiple atlases allows one to better take into account anatomical variability and thereby to compensate, to some extent, for the errors of matching to each individual atlas. In this work, a more efficient (in speed and memory) implementation is proposed of one of the best multiatlas label fusion algorithms in order to obtain a labeling of the input image. The algorithm is applied to the problem of segmentation of brain MR images into 43 anatomical regions with the use of the publicly available IBSR database, in contrast to the original work, where the authors provide test results for the problem of extraction of a single anatomical structure, the hippocampus.