Development and validation of a quality control procedure for automatic segmentation of hippocampal subfields.

ABSTRACT: Automatic segmentation methods for in vivo magnetic resonance imaging are increasing in popularity because of their high efficiency and reproducibility. However, automatic methods can be perfectly reliable and consistently wrong, and the validity of automatic segmentation methods cannot be taken for granted. Quality control (QC) by trained and reliable human raters is necessary to ensure the validity of automatic measurements. Yet QC practices for applied neuroimaging research are underdeveloped. We report a detailed QC and correction procedure to accompany our validated atlas for hippocampal subfield segmentation. We document a two-step QC procedure for identifying segmentation errors, along with a taxonomy of errors and an error severity rating scale. This detailed procedure has high bet

SUBMITTER: Canada KL 

PROVIDER: S-EPMC10524242 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature