Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting.
Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting.
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用于在低数据设置中生成基于合成纤维束成像的捆绑模板的变分自动编码器。
DOI：
10.1109/embc40787.2023.10340009
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Thompson,PaulM
中科院分区：
文献类型：
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作者：
Feng,Yixue;Chandio,BramshQ;Thomopoulos,SophiaI;Chattopadhyay,Tamoghna;Thompson,PaulM
从全脑纤维束成像生成的白色物质束通常使用具有标准图谱的自动分割方法进行处理。从数百个受试者中生成的数据库，创建起来非常耗时，并且难以应用于所有人群。在这项研究中，我们扩展了我们之前的工作，使用深度生成模型-卷积变分自动编码器-将复杂和数据密集型的流线映射到低维潜在空间，给定ADNI 3数据集的50个受试者的有限样本量，使用流线嵌入的核密度估计（KDE）生成合成的特定于人群的束模板。我们通过计算束形状度量进行了定量形状分析，发现我们的束模板比来自年轻健康成年人的原始分割中使用的图谱数据更好地捕获束的形状分布。我们进一步展示了使用我们的框架从全脑纤维束图直接束分割。
White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.