Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, Psychiatry & Anatomy, School of Medicine,College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91TK33, Galway, Ireland.
College of Engineering and Informatics, National University of Ireland Galway, H91TK33, Galway, Ireland.
Brain Imaging Behav. 2018 Dec;12(6):1678-1695. doi: 10.1007/s11682-018-9835-y.
Manual tracing of magnetic resonance imaging (MRI) represents the gold standard for segmentation in clinical neuropsychiatric research studies, however automated approaches are increasingly used due to its time limitations. The accuracy of segmentation techniques for subcortical structures has not been systematically investigated in large samples. We compared the accuracy of fully automated [(i) model-based: FSL-FIRST; (ii) patch-based: volBrain], semi-automated (FreeSurfer) and stereological (Measure®) segmentation techniques with manual tracing (ITK-SNAP) for delineating volumes of the caudate (easy-to-segment) and the hippocampus (difficult-to-segment). High resolution 1.5 T T1-weighted MR images were obtained from 177 patients with major psychiatric disorders and 104 healthy participants. The relative consistency (partial correlation), absolute agreement (intraclass correlation coefficient, ICC) and potential technique bias (Bland-Altman plots) of each technique was compared with manual segmentation. Each technique yielded high correlations (0.77-0.87, p < 0.0001) and moderate ICC's (0.28-0.49) relative to manual segmentation for the caudate. For the hippocampus, stereology yielded good consistency (0.52-0.55, p < 0.0001) and ICC (0.47-0.49), whereas automated and semi-automated techniques yielded poor ICC (0.07-0.10) and moderate consistency (0.35-0.62, p < 0.0001). Bias was least using stereology for segmentation of the hippocampus and using FreeSurfer for segmentation of the caudate. In a typical neuropsychiatric MRI dataset, automated segmentation techniques provide good accuracy for an easy-to-segment structure such as the caudate, whereas for the hippocampus, a reasonable correlation with volume but poor absolute agreement was demonstrated. This indicates manual or stereological volume estimation should be considered for studies that require high levels of precision such as those with small sample size.
手动追踪磁共振成像 (MRI) 是临床神经精神研究中分割的金标准，但由于时间限制，自动化方法越来越多地被使用。在大样本中，尚未系统研究亚皮质结构分割技术的准确性。我们比较了全自动 [（i）基于模型：FSL-FIRST；（ii）基于斑块：volBrain]、半自动（FreeSurfer）和立体学（Measure®）分割技术与手动追踪（ITK-SNAP）在勾画尾状核（易于分割）和海马体（难以分割）体积方面的准确性。从 177 名患有主要精神疾病的患者和 104 名健康参与者中获得高分辨率 1.5 T T1 加权 MR 图像。比较了每种技术与手动分割的相对一致性（偏相关）、绝对一致性（组内相关系数，ICC）和潜在技术偏差（Bland-Altman 图）。与手动分割相比，每种技术都产生了高度相关（0.77-0.87，p < 0.0001）和中度 ICC（0.28-0.49），用于尾状核。对于海马体，立体学产生了良好的一致性（0.52-0.55，p < 0.0001）和 ICC（0.47-0.49），而自动和半自动技术产生了较差的 ICC（0.07-0.10）和中度一致性（0.35-0.62，p < 0.0001）。对于海马体的分割，立体学的偏差最小，对于尾状核的分割，FreeSurfer 的偏差最小。在典型的神经精神 MRI 数据集中，自动化分割技术为易于分割的结构（如尾状核）提供了良好的准确性，而对于海马体，则表现出与体积的合理相关性，但绝对一致性较差。这表明对于需要高精度的研究，如样本量较小的研究，应考虑手动或立体学体积估计。
