Measurement of brain structures with artificial neural networks: Two- and three-dimensional applications
Measurement of brain structures with artificial neural networks: Two- and three-dimensional applications
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DOI：
10.1148/radiology.211.3.r99ma07781
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发表时间：
1999-06-01
期刊：
影响因子：
19.7
通讯作者：
Yuh, WTC
中科院分区：
文献类型：
--
作者：
Magnotta, VA;Heckel, D;Yuh, WTC
用这种方法识别。目的：评估人工神经网络（ANN）识别大脑结构的能力。这个人工神经网络被应用于；后处理磁性;磁共振（MR）图像在二维和三维应用中分割各种大脑结构。材料和方法：根据经验设计了一个人工神经网络来定义胼胝体、全脑、尾状核和壳核。人工分割作为人工神经网络的训练集。人工神经网络在三分之二的人工分割图像上进行训练，并在剩下的三分之一上进行测试。两名技术人员将人工神经网络的可靠性与人工分割进行了比较。结果：人工神经网络与两名技术人员一样容易识别大脑结构。与技术人员相比，人工神经网络对胼胝体的可靠性为0.96，对整个大脑的可靠性为0.95，对尾状核的可靠性为0.86和0.93（左），对壳核的可靠性为0.71和0.88（左）。结论：人工神经网络能够识别本研究中使用的结构，以及两位技术人员。人工神经网络可以更快地做到这一点，而且不会产生较大的漂移。几个,其他;用这种方法也可以很容易地识别皮层和皮层下结构。
identified with this method. PURPOSE: To evaluate the ability of an artifical-neural network (ANN) to identify brain structures. This ANN was applied : to;postprocess magnetic;resonance (MR) images to segment Various brain structures in both two- and three-dimensional applications.MATERIALS AND METHODS: An ANN was designed that learned from experience to define the corpus callosum, whole brain, caudate, and putamen. Manual segmentation was used asa 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.RESULTS: 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.CONCLUSION:The ANN was able to identify the structures used inthisstudy 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.