Improving performance robustness of subject-based brain segmentation software.

ABSTRACT:

Purpose

Artificial intelligence (AI)-based image analysis tools to quantify the brain have become commercialized. However, insufficient data for learning and scanner specificity is a limitation for achieving high quality. In the present study, the performance of personalized brain segmentation software when applied to multicenter data using an AI model trained on data from a single institution was improved.

Methods

Preindicators of brain white matter (WM) information from the training dataset were utilized for preprocessing. During learning, data of cognitively normal (CN) individuals from a single center were utilized, and data of CN individuals and Alzheimer disease (AD) patients enrolled in multiple centers were considered the test set.

Results

The preprocessing ba

SUBMITTER: Park JH 

PROVIDER: S-EPMC10295817 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature