A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling *

Accurate segmentation of the brain into gray matter, white matter, and cerebrospinal fluid using mag-netic resonance (MR) imaging is critical for visualization and quantification of brain anatomy. Compared to 3T MR images, 7T MR images exhibit higher tissue contrast that is contributive to accurate tissue de-lineation for training segmentation models. In this paper, we propose a cascaded nested network (CaNes-Net) for segmentation of 3T brain MR images, trained by tissue labels delineated from the corresponding 7T images. We first train a nested network (Nes-Net) for a rough segmentation. The second Nes-Net uses tissue-specific geodesic distance maps as contextual information to refine the segmentation. This process is iterated to build CaNes-Net with a cascade of Nes-Net modules to gradually refine the seg-mentation. To alleviate the misalignment between 3T and corresponding 7T MR images, we incorporate a correlation coefficient map to allow well-aligned voxels to play a more important role in supervising the training process. We compared CaNes-Net with SPM and FSL tools, as well as four deep learning models on 18 adult subjects and the ADNI dataset. Our results indicate that CaNes-Net reduces segmentation er -rors caused by the misalignment and improves segmentation accuracy substantially over the competing methods. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A cascaded nested network (CaNes-Net) is proposed for brain MR image segmentation at 3T, trained by tissue labels delineated from 7T images, reducing segmentation errors caused by misalignment and improving accuracy substantially.