Hierarchical Parcellation of the Cerebellum.
Hierarchical Parcellation of the Cerebellum.
复制标题
小脑的分层分区。
DOI：
10.1007/978-3-030-32248-9_54
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Prince,JerryL
中科院分区：
文献类型：
--
作者：
Han,Shuo;Carass,Aaron;Prince,JerryL
在MR图像中小脑的包裹已被用于研究与运动和认知功能的区域关联。尽管小脑的划分是按层次定义的，小脑可以被分成多个叶，并且这些叶可以被进一步分成多个小叶-以前的包裹小脑的自动方法没有利用该信息。在这项工作中，我们提出了一种基于卷积神经网络（CNN）的方法，以显式地将小脑的分层组织。该网络以树结构构建，每个节点代表小脑区域，并具有进一步将该区域细分为更精细子结构的子节点。因此，我们的CNN知道小脑的层次结构。此外，通过选择树节点来表示给定训练样本的层次属性，我们的网络可以使用标记为不同层次深度的异构训练数据进行训练。所提出的方法进行了比较，一个国家的最先进的小脑包裹网络。我们的方法显示出有前途的结果，作为第一个parcellation方法，考虑到小脑的层次组织。
Parcellation of the cerebellum in an MR image has been used to study regional associations with both motion and cognitive functions. Despite the fact that the division of the cerebellum is defined hierarchically—i.e., the cerebellum can be divided into lobes and the lobes can be further divided into lobules—previous automatic methods to parcellate the cerebellum do not utilize this information. In this work, we propose a method based on convolutional neural networks (CNNs) to explicitly incorporate the hierarchical organization of the cerebellum. The network is constructed in a tree structure with each node representing a cerebellar region and having child nodes that further subdivide the region into finer substructures. Thus, our CNN is aware of the hierarchical organization of the cerebellum. Furthermore, by selecting tree nodes to represent the hierarchical properties of a given training sample, our network can be trained with heterogeneous training data that are labeled to different hierarchical depths. The proposed method was compared with a state-of-the-art cerebellum parcellation network. Our approach shows promising results as a first parcellation method to take the cerebellar hierarchical organization into consideration.