| Title: | Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation |
| Journal: | Scientific Reports |
| Published: | 31 Jan 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35102199/ |
| DOI: | https://doi.org/10.1038/s41598-022-05583-3 |
| URL: | https://www.nature.com/articles/s41598-022-05583-3.pdf |
| Citations: | 7 (6 in last 2 years) as of 8 Aug 2024 |
Publication 12576
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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 pipeline (ReSeg) to examine whether reliability depends on choice of deep learning method. Structural MRI scans were collected from 110 participants under rest and active head motion and were evaluated for image quality by radiologists. Compared to FreeSurfer, deep learning-based methods provided more consistent segmentations across different levels of image quality, suggesting that they also have the advantage of providing more reliable whole-brain segmentations of MR images corrupted by motion-induced artifacts, and provide evidence for their practical applicability in the study of brain structural alterations in health and disease.</p>
14 Keywords
- Adult
- Artifacts
- Brain
- Deep Learning
- Female
- Head
- Head Movements
- Humans
- Image Processing, Computer-Assisted
- Magnetic Resonance Imaging
- Male
- Neuroimaging
- Reproducibility of Results
- Young Adult
7 Authors
- Péter Kemenczky
- Pál Vakli
- Eszter Somogyi
- István Homolya
- Petra Hermann
- Viktor Gál
- Zoltán Vidnyánszky
1 Application
| Application ID | Title |
|---|---|
| 27236 | Exploring the relationship between body weight and cognitive functions across the lifespan using deep learning techniques |
Enabling scientific discoveries that improve human health