Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network.

ABSTRACT:

Background

Whole brain delineation (WBD) is utilized in neuroimaging analysis for data preprocessing and deriving whole brain image metrics. Current automated WBD techniques for analysis of preclinical brain MRI data show limited accuracy when images present with significant neuropathology and anatomical deformations, such as that resulting from organophosphate intoxication (OPI) and Alzheimer's Disease (AD), and inadequate generalizability.

Methods

A modified 2D U-Net framework was employed for WBD of MRI rodent brains, consisting of 27 convolutional layers, batch normalization, two dropout layers and data augmentation, after training parameter optimization. A total of 265 T₂-weighted 7.0 T MRI scans were utilized for the study, including 125 scans of an OPI rat

SUBMITTER: Porter VA 

PROVIDER: S-EPMC11000587 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature