Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation.

ABSTRACT: Due to their robustness and speed, recently developed deep learning-based methods have the potential to provide a faster and hence more scalable alternative to more conventional neuroimaging analysis pipelines in terms of whole-brain segmentation based on magnetic resonance (MR) images. These methods were also shown to have higher test-retest reliability, raising the possibility that they could also exhibit superior head motion tolerance. We investigated this by comparing the effect of head motion-induced artifacts in structural MR images on the consistency of segmentation performed by FreeSurfer and recently developed deep learning-based methods to a similar extent. We used state-of-the art neural network models (FastSurferCNN and Kwyk) and developed a new whole-brain segmentation pipelin

SUBMITTER: Kemenczky P 

PROVIDER: S-EPMC8803940 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature