Subject-Specific Structural Parcellations Based on Randomized AB-divergences.
Subject-Specific Structural Parcellations Based on Randomized AB-divergences.
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DOI：
10.1007/978-3-319-66182-7_47
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发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
Verma R
中科院分区：
文献类型：
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作者：
Honnorat N;Parker D;Tunç B;Davatzikos C;Verma R
脑分割提供了一种在较小区域接近大脑的方法。它还在连接体的创建中提供了适当的降维。大多数创建连接体的方法都是从将单个扫描记录到模板开始的，然后将其分割。数据处理通常以将单个扫描投影到分割上以提取单个生物标志物（诸如连接性签名）而结束。在此过程中，配准误差会显著改变生物标志物的质量。在本文中，我们提出了一种混合的方法来减轻这个问题的大脑parcellation。我们使用基于扩散MRI（dMRI）的结构连接性措施来驱动解剖学先前分割的细化。我们的方法在原生主题空间中生成高度连贯的结构包裹，同时保持整个人群的可解释性和对应性。这一目标是通过在个体dMRI扫描之前配准群体范围的解剖结构并为每个体素生成连接签名来实现的。然后，通过根据体素连接签名之间的相似性对大脑进行重新分组，同时约束包裹的数量，来变形解剖先验。我们调查了一个广泛的家庭的签名相似性被称为AB分歧，并解释如何分歧适应我们的分割任务可以选择。这种分歧用于使用两种基于图形的方法对高分辨率数据集进行分割。所获得的有希望的结果表明，我们的方法产生连贯的包裹和更强的连接比原来的解剖先验。
Brain parcellation provides a means to approach the brain in smaller regions. It also affords an appropriate dimensionality reduction in the creation of connectomes. Most approaches to creating connectomes start with registering individual scans to a template, which is then parcellated. Data processing usually ends with the projection of individual scans onto the parcellation for extracting individual biomarkers, such as connectivity signatures. During this process, registration errors can significantly alter the quality of biomarkers. In this paper, we propose to mitigate this issue with a hybrid approach for brain parcellation. We use diffusion MRI (dMRI) based structural connectivity measures to drive the refinement of an anatomical prior parcellation. Our method generates highly coherent structural parcels in native subject space while maintaining interpretability and correspondences across the population. This goal is achieved by registering a population-wide anatomical prior to individual dMRI scan and generating connectivity signatures for each voxel. The anatomical prior is then deformed by re-parcellating the brain according to the similarity between voxel connectivity signatures while constraining the number of parcels. We investigate a broad family of signature similarities known as AB-divergences and explain how a divergence adapted to our segmentation task can be selected. This divergence is used for parcellating a high-resolution dataset using two graph-based methods. The promising results obtained suggest that our approach produces coherent parcels and stronger connectomes than the original anatomical priors.