Schnack H G, Hulshoff Pol H E, Baaré W F, Viergever M A, Kahn R S
Department of Psychiatry, University Medical Center Utrecht, The Netherlands.
Neuroimage. 2001 Jul;14(1 Pt 1):95-104. doi: 10.1006/nimg.2001.0800.
An algorithm was developed that automatically segments the lateral and third ventricles from T1-weighted 3-D-FFE MR images of the human brain. The algorithm is based upon region-growing and mathematical morphology operators and starts from a coarse binary total brain segmentation, which is obtained from the 3-D-FFE image. Anatomical knowledge of the ventricular system has been incorporated into the method in order to find all constituting parts of the system, even if they are disconnected, and to avoid inclusion of nonventricle cerebrospinal fluid (CSF) regions. A test of the method on a synthetic MR brain image produced a segmentation overlap of 0.98 between the simulated ventricles ("model") and those defined by the algorithm. Further tests were performed on a large data set of 227 1.5 T MR brain images. The algorithm yielded useful results for 98% of the images. The automatic segmentations had intra-class correlation coefficients of 0.996 for the lateral ventricles and 0.86 for the third ventricle, with manually edited segmentations. Comparison of ventricular volumes of schizophrenia patients compared with those of healthy control subjects showed results in agreement with the literature.
开发了一种算法，可从人脑的T1加权3D-FFE MR图像中自动分割侧脑室和第三脑室。该算法基于区域生长和数学形态学算子，从通过3D-FFE图像获得的粗略二进制全脑分割开始。脑室系统的解剖学知识已被纳入该方法中，以便找到系统的所有组成部分，即使它们是断开的，并避免包含非脑室脑脊液（CSF）区域。在合成MR脑图像上对该方法进行测试，模拟脑室（“模型”）与算法定义的脑室之间的分割重叠率为0.98。在包含227张1.5T MR脑图像的大数据集上进行了进一步测试。该算法对98%的图像产生了有用的结果。自动分割与手动编辑分割相比，侧脑室的类内相关系数为0.996，第三脑室的类内相关系数为0.86。与健康对照受试者相比，精神分裂症患者脑室体积的比较结果与文献一致。
