Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
Institute of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary.
Sci Rep. 2022 Jan 31;12(1):1618. doi: 10.1038/s41598-022-05583-3.
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.
由于其稳健性和速度，最近开发的基于深度学习的方法有可能为基于磁共振（MR）图像的全脑分割提供一种比更传统的神经影像学分析管道更快、因此更具可扩展性的替代方案。这些方法还显示出更高的测试-重测可靠性，这提高了它们也可能表现出更高的头部运动耐受性的可能性。我们通过比较头部运动引起的磁共振图像伪影对 FreeSurfer 和最近开发的基于深度学习的方法执行的分割一致性的影响，来研究这一点。我们使用了最先进的神经网络模型（FastSurferCNN 和 Kwyk）和开发了一个新的全脑分割管道（ReSeg），以检查可靠性是否取决于深度学习方法的选择。从 110 名参与者在休息和主动头部运动下采集结构磁共振成像扫描，并由放射科医生评估图像质量。与 FreeSurfer 相比，基于深度学习的方法在不同图像质量水平下提供了更一致的分割，这表明它们还具有提供更可靠的全脑分割的优势，这些分割受到运动伪影的影响，并且为它们在健康和疾病中研究大脑结构变化的实际应用提供了证据。
