Piraino D W, Amartur S C, Richmond B J, Schils J P, Thome J M, Weber P B
Department of Radiology, Cleveland Clinic Foundation.
Proc Annu Symp Comput Appl Med Care. 1991:470-2.
Signal intensities from intermediate and T2 weighted spin echo images of the brain were used as inputs into an artificial neural network (ANN). The signal intensities were used to train the network to recognize anatomically-important segments. The ANN was a self-organizing map (SOM) neural network which develops a continuous topographical map of the signal intensities within the two images. The neural network segmented images demonstrated good correlation with white matter, gray matter, and cerebral spinal fluid (CSF) spaces. This technique was rated better than manual thresholding of the intermediate images, but not as good as manual thresholding of the T2 weighted images.
大脑的中间加权和T2加权自旋回波图像的信号强度被用作人工神经网络（ANN）的输入。信号强度用于训练网络以识别具有解剖学重要性的节段。该人工神经网络是一个自组织映射（SOM）神经网络，它在这两张图像内生成信号强度的连续地形图。神经网络分割的图像与白质、灰质和脑脊髓液（CSF）空间显示出良好的相关性。该技术的评分优于中间图像的手动阈值处理，但不如T2加权图像的手动阈值处理。
