Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach.

ABSTRACT: Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These appro

SUBMITTER: Rushmore RJ 

PROVIDER: S-EPMC9562126 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature