Harmonized neonatal brain MR image segmentation model for cross-site datasets.

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

SUBMITTER: Chen J 

PROVIDER: S-EPMC9371447 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature