Cohen G, Andreasen N C, Alliger R, Arndt S, Kuan J, Yuh W T, Ehrhardt J
Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City 52242.
Psychiatry Res. 1992 May;45(1):33-51. doi: 10.1016/0925-4927(92)90012-s.
A technique is described for classifying brain tissue into three components: gray matter, white matter, and cerebrospinal fluid. This technique uses simultaneously registered proton density and T2-weighted images. Samples of each of the three types of tissue are identified on both image sets and used as "training classes"; these tissue samples are then used to generate a linear discriminant function, which is used to classify the remaining pixels in the image data set. Effects of varying the location and number of training classes have been explored; six pairs of training classes have been found to yield a suitable classification. Interrater and test-retest reliability have been examined and found to be good. Intrascanner and interscanner reproducibility has also been evaluated; classification rates are reproducible within the same individual when the same scanner is used, but in this study poor reproducibility occurs when the same individual is scanned on two different scanners. The validity of the technique has been tested by examining correlations between traced and segmented regions of interest, evaluating correlations with age, and conducting phantom studies, in addition to using visual inspection of the classified images as an indication of face validity. From all four perspectives, the method has been found to have good validity. Additional applications, strengths, and limitations are discussed.
本文描述了一种将脑组织分为灰质、白质和脑脊液三种成分的技术。该技术使用同时配准的质子密度图像和T2加权图像。在这两个图像集上识别出这三种组织类型的样本，并将其用作“训练类别”；然后使用这些组织样本生成线性判别函数，该函数用于对图像数据集中的其余像素进行分类。研究了改变训练类别的位置和数量的影响；已发现六对训练类别可产生合适的分类。已检查评分者间信度和重测信度，结果良好。还评估了扫描仪内和扫描仪间的可重复性；使用同一台扫描仪时，同一个体的分类率具有可重复性，但在本研究中，当同一个体在两台不同的扫描仪上进行扫描时，可重复性较差。除了将分类图像的视觉检查作为表面效度的指标外，还通过检查感兴趣区域的手动追踪和分割之间的相关性、评估与年龄的相关性以及进行体模研究来测试该技术的效度。从所有四个角度来看，该方法都具有良好的效度。此外，还讨论了该方法的其他应用、优点和局限性。
