Abstract
Accurate segmentation of white matter, gray matter and cerebrospinal fluid from neonatal brain MR images is of great importance in characterizing early brain development. Deep-learning-based methods have been successfully applied to neonatal brain MRIs with superior performance if testing subjects were acquired with the same imaging protocols/scanners as training subjects. However, for the testing subjects acquired with different imaging protocols/scanners, they cannot achieve accurate segmentation results due to large appearance/pattern differences between the testing and training subjects. Besides, imaging artifacts, like head motion, which are inevitable during the imaging acquisition process, also pose a challenge for the segmentation methods. To address these issues, in this paper, we propose a harmonized neonatal brain MR image segmentation model that harmonizes testing images acquired by different protocols/scanners into the domain of training images through a cycle-consistent generative adversarial network (CycleGAN). Meanwhile, the artifacts can be largely alleviated during the harmonization. Then, a densely-connected U-Net based segmentation model trained in the domain of training images can be applied robustly for segmenting the harmonized testing images. Comparisons with existing methods illustrate the better performance of the proposed method on neonatal brain MR images from cross-sites, a grand segmentation challenge, as well as images with artifacts.