Gibicar Adam, Moody Alan R, Khademi April
Electrical, Computer and Biomedical Engineering Department, Ryerson University, Toronto, ON, Canada.
Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Front Aging Neurosci. 2021 Apr 29;13:644137. doi: 10.3389/fnagi.2021.644137. eCollection 2021.
To perform brain asymmetry studies in large neuroimaging archives, reliable and automatic detection of the interhemispheric fissure (IF) is needed to first extract the cerebral hemispheres. The detection of the IF is often referred to as mid-sagittal plane estimation, as this plane separates the two cerebral hemispheres. However, traditional planar estimation techniques fail when the IF presents a curvature caused by existing pathology or a natural phenomenon known as brain torque. As a result, midline estimates can be inaccurate. In this study, a fully unsupervised midline estimation technique is proposed that is comprised of three main stages: head angle correction, control point estimation and midline generation. The control points are estimated using a combination of intensity, texture, gradient, and symmetry-based features. As shown, the proposed method automatically adapts to IF curvature, is applied on a slice-to-slice basis for more accurate results and also provides accurate delineation of the midline in the septum pellucidum, which is a source of failure for traditional approaches. The method is compared to two state-of-the-art methods for midline estimation and is validated using 75 imaging volumes (3,000 imaging slices) acquired from 38 centers of subjects with dementia and vascular disease. The proposed method yields the lowest average error across all metrics: Hausdorff distance (HD) was 0.32 ± 0.23, mean absolute difference (MAD) was 1.10 ± 0.38 mm and volume difference was 7.52 ± 5.40 and 5.35 ± 3.97 ml, for left and right hemispheres, respectively. Using the proposed method, the midline was extracted for 5,360 volumes (275K images) from 83 centers worldwide, acquired by GE, Siemens and Philips scanners. An asymmetry index was proposed that automatically detected outlier segmentations (which were <1% of the total dataset). Using the extracted hemispheres, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and significant differences in biomarker means were found across SCI and MCI and SCI and AD.
为了在大型神经影像档案中进行脑不对称性研究，首先需要可靠且自动地检测大脑半球间裂（IF）以提取大脑半球。IF的检测通常被称为中矢状面估计，因为该平面将两个大脑半球分开。然而，当IF由于现有病变或一种称为脑扭矩的自然现象而出现曲率时，传统的平面估计技术就会失效。结果，中线估计可能不准确。在本研究中，提出了一种完全无监督的中线估计技术，该技术由三个主要阶段组成：头部角度校正、控制点估计和中线生成。使用强度、纹理、梯度和基于对称性的特征组合来估计控制点。如图所示，所提出的方法能自动适应IF曲率，逐片应用以获得更准确的结果，并且还能准确描绘透明隔中的中线，而这是传统方法失败的一个原因。将该方法与两种用于中线估计的最先进方法进行了比较，并使用从38个患有痴呆症和血管疾病的受试者中心获取的75个成像容积（约3000个成像切片）进行了验证。所提出的方法在所有指标上产生的平均误差最低：对于左、右半球，豪斯多夫距离（HD）为0.32±0.23，平均绝对差（MAD）为1.10±0.38毫米，体积差分别为7.52±5.40和5.35±3.97毫升。使用所提出的方法，从全球83个中心获取的、由GE、西门子和飞利浦扫描仪采集的5360个容积（约275K图像）中提取了中线。提出了一种不对称指数，该指数能自动检测异常分割（占总数据集的比例小于1%）。使用提取的半球，在一个痴呆症队列中分析了正常外观脑实质（NABM）的半球不对称纹理生物标志物，并且发现在SCI与MCI以及SCI与AD之间生物标志物均值存在显著差异。
