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首页> 外文期刊>Medical image analysis >Automatic recognition of cortical sulci of the human brain using a congregation of neural networks.
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Automatic recognition of cortical sulci of the human brain using a congregation of neural networks.

机译：使用神经网络的集合自动识别人脑的皮质沟。

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This paper describes a complete system allowing automatic recognition of the main sulci of the human cortex. This system relies on a preprocessing of magnetic resonance images leading to abstract structural representations of the cortical folding patterns. The representation nodes are cortical folds, which are given a sulcus name by a contextual pattern recognition method. This method can be interpreted as a graph matching approach, which is driven by the minimization of a global function made up of local potentials. Each potential is a measure of the likelihood of the labelling of a restricted area. This potential is given by a multi-layer perceptron trained on a learning database. A base of 26 brains manually labelled by a neuroanatomist is used to validate our approach. The whole system developed for the right hemisphere is made up of 265 neural networks. The mean recognition rate is 86% for the learning base and 76% for a generalization base, which is very satisfying considering the current weak understanding of the variability of the cortical folding patterns.
机译：本文介绍了一个完整的系统，可以自动识别人类皮层的主要沟。该系统依赖于磁共振图像的预处理，从而导致皮层折叠模式的抽象结构表示。表示节点是皮质褶皱，通过上下文模式识别方法为其指定一个沟名。这种方法可以解释为一种图形匹配方法，它是由最小化由局部电位组成的全局函数驱动的。每个电势都是对限制区域进行标记的可能性的度量。这种潜力是由在学习数据库上训练的多层感知器赋予的。由神经解剖学家手动标记的26个大脑的基地用于验证我们的方法。为右半球开发的整个系统由265个神经网络组成。学习基础的平均识别率为86％，泛化基础的平均识别率为76％，考虑到当前对皮质折叠模式可变性的了解不多，这非常令人满意。

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