Oguz Ipek, Styner Martin, Sanchez Mar, Shi Yundi, Sonka Milan
Iowa Institute for Biomedical Imaging, Dept. of Electrical & Computer Engineering, and Ophthalmology & Visual Sciences, The Univ. of Iowa, Iowa City, IA.
Dept. of Psychiatry and Computer Science, Univ. of North Carolina, Chapel Hill, NC.
Proc SPIE Int Soc Opt Eng. 2015;9413. doi: 10.1117/12.2082327.
Cortical thickness and surface area are important morphological measures with implications for many psychiatric and neurological conditions. Automated segmentation and reconstruction of the cortical surface from 3D MRI scans is challenging due to the variable anatomy of the cortex and its highly complex geometry. While many methods exist for this task in the context of the human brain, these methods are typically not readily applicable to the primate brain. We propose an innovative approach based on our recently proposed human cortical reconstruction algorithm, LOGISMOS-B, and the Laplace-based thickness measurement method. Quantitative evaluation of our approach was performed based on a dataset of T1- and T2-weighted MRI scans from 12-month-old macaques where labeling by our anatomical experts was used as independent standard. In this dataset, LOGISMOS-B has an average signed surface error of 0.01 ± 0.03mm and an unsigned surface error of 0.42 ± 0.03mm over the whole brain. Excluding the rather problematic temporal pole region further improves unsigned surface distance to 0.34 ± 0.03mm. This high level of accuracy reached by our algorithm even in this challenging developmental dataset illustrates its robustness and its potential for primate brain studies.
皮质厚度和表面积是重要的形态学测量指标,对许多精神和神经疾病具有重要意义。由于皮质解剖结构的多样性及其高度复杂的几何形状,从三维磁共振成像(MRI)扫描中自动分割和重建皮质表面具有挑战性。虽然在人类大脑的背景下有许多方法可用于此任务,但这些方法通常不易应用于灵长类动物大脑。我们基于我们最近提出的人类皮质重建算法LOGISMOS - B和基于拉普拉斯的厚度测量方法,提出了一种创新方法。我们的方法基于12个月大猕猴的T1加权和T2加权MRI扫描数据集进行了定量评估,其中我们的解剖学专家的标记被用作独立标准。在这个数据集中,LOGISMOS - B在全脑上的平均有符号表面误差为0.01±0.03毫米,无符号表面误差为0.42±0.03毫米。排除问题较大的颞极区域后,无符号表面距离进一步提高到0.34±0.03毫米。即使在这个具有挑战性的发育数据集中,我们的算法达到的这种高精度水平也说明了其稳健性及其在灵长类动物大脑研究中的潜力。