Jackson E F, Narayana P A, Wolinsky J S, Doyle T J
Department of Radiology, University of Texas Medical School, Houston 77030.
J Comput Assist Tomogr. 1993 Mar-Apr;17(2):200-5. doi: 10.1097/00004728-199303000-00007.
The accuracy and reproducibility of dual-contrast segmentation based on nonparametric feature map analysis have been investigated in a multicomponent gelatin phantom. The root mean square errors in volume ranged from 0.02 cm3 for small volumes to 3.8 cm3 for larger volumes, with a mean error of 0.97 cm3. Average inter- and intraobserver coefficients of variation were found to be < 7% for all compartments. To evaluate the reproducibility of segmentation of clinical image data, volumes of total brain, CSF, and multiple sclerosis (MS) lesions were obtained from five image sets of MS patients. Inter- and intraobserver coefficients of variations were computed for the patient data and were found to be < 5% for brain, 17% for CSF, and 20% for MS lesions. Such variations were found to be reduced by appropriate preprocessing of the images.
基于非参数特征图分析的双对比分割在多成分明胶模型中的准确性和可重复性已得到研究。体积的均方根误差范围从小体积的0.02立方厘米到较大体积的3.8立方厘米，平均误差为0.97立方厘米。所有区域的观察者间和观察者内平均变异系数均<7%。为评估临床图像数据分割的可重复性，从5组多发性硬化症（MS）患者的图像集中获取了全脑、脑脊液和MS病灶的体积。计算了患者数据的观察者间和观察者内变异系数，发现脑的变异系数<5%，脑脊液的为17%，MS病灶的为20%。发现通过对图像进行适当的预处理可减少此类变异。
