Magnotta V A, Heckel D, Andreasen N C, Cizadlo T, Corson P W, Ehrhardt J C, Yuh W T
Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA.
Radiology. 1999 Jun;211(3):781-90. doi: 10.1148/radiology.211.3.r99ma07781.
To evaluate the ability of an artificial neural network (ANN) to identify brain structures. This ANN was applied to postprocessed magnetic resonance (MR) images to segment various brain structures in both two- and three-dimensional applications.
An ANN was designed that learned from experience to define the corpus callosum, whole brain, caudate, and putamen. Manual segmentation was used as a training set for the ANN. The ANN was trained on two-thirds of the manually segmented images and was tested on the remaining one-third. The reliability of the ANN was compared against manual segmentations by two technicians.
The ANN was able to identify the brain structures as readily and as well as did the two technicians. Reliability of the ANN compared with the technicians was 0.96 for the corpus callosum, 0.95 for the whole brain, 0.86 (right) and 0.93 (left) for the caudate, and 0.71 (right) and 0.88 (left) for the putamen.
The ANN was able to identify the structures used in this study as well as did the two technicians. The ANN could do this much more rapidly and without rater drift. Several other cortical and subcortical structures could also be readily identified with this method.
评估人工神经网络（ANN）识别脑结构的能力。该人工神经网络应用于后处理的磁共振（MR）图像，以在二维和三维应用中分割各种脑结构。
设计了一个从经验中学习以定义胼胝体、全脑、尾状核和壳核的人工神经网络。手动分割用作人工神经网络的训练集。人工神经网络在三分之二的手动分割图像上进行训练，并在其余三分之一的图像上进行测试。通过两名技术人员将人工神经网络的可靠性与手动分割进行比较。
人工神经网络能够像两名技术人员一样轻松且出色地识别脑结构。与技术人员相比，人工神经网络在胼胝体上的可靠性为0.96，全脑为0.95，尾状核右侧为0.86、左侧为0.93，壳核右侧为0.71、左侧为0.88。
人工神经网络能够像两名技术人员一样出色地识别本研究中使用的结构。人工神经网络可以更快地做到这一点，而且不会出现评分者偏差。使用这种方法还可以轻松识别其他几个皮质和皮质下结构。
