Haller J W, Christensen G E, Joshi S C, Newcomer J W, Miller M I, Csernansky J G, Vannier M W
Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA.
Radiology. 1996 Jun;199(3):787-91. doi: 10.1148/radiology.199.3.8638006.
To determine the repeatability and validity of a pattern-matching method for the segmentation and measurement of hippocampi on magnetic resonance (MR) images.
Comparable two-dimensional MR images obtained in 18 subjects (nine healthy control subjects [six men, three women; aged 24-54 years] and nine patients with schizophrenia [six men, three women; aged 22-61 years]) were twice segmented manually and twice segmented by using pattern matching with digital atlas transformation. The atlas transformation was accomplished in two steps: global followed by local matching. Global matching was performed with use of landmarks; local matching was performed with use of a viscous fluid model.
The mean percentage of difference between two atlas-based measurements was 1.33% +/- 1.23 (+/- standard deviation); that between two manual measurements was 4.67% +/- 4.71. The validity of the atlas transformation measurements was demonstrated by means of the high correlation (intraclass correlation coefficient = .96) with manual segmentation measurements. Schizophrenic hippocampal areas tended to be smaller; however, no differences in hippocampal shape were found between patients with schizophrenia and patients with control subjects.
General pattern matching of a digital brain atlas to an individual MR image is a mathematically robust method of measurement that is reproducible and less variable than manual measurement.
确定一种用于磁共振（MR）图像上海马体分割与测量的模式匹配方法的可重复性和有效性。
对18名受试者（9名健康对照者[6名男性，3名女性；年龄24 - 54岁]和9名精神分裂症患者[6名男性，3名女性；年龄22 - 61岁]）获得的可比二维MR图像进行两次手动分割以及两次使用数字图谱变换的模式匹配分割。图谱变换分两步完成：先全局匹配再局部匹配。全局匹配使用地标进行；局部匹配使用粘性流体模型进行。
两次基于图谱测量之间的平均差异百分比为1.33%±1.23（±标准差）；两次手动测量之间的平均差异百分比为4.67%±4.71。通过与手动分割测量的高相关性（组内相关系数 = 0.96）证明了图谱变换测量的有效性。精神分裂症患者的海马体区域往往较小；然而，精神分裂症患者与对照受试者之间未发现海马体形状存在差异。
将数字脑图谱与个体MR图像进行一般模式匹配是一种数学上稳健的测量方法，与手动测量相比具有可重复性且变异性较小。
