Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
National Imaging Facility, Brisbane, QLD, Australia.
Neuroinformatics. 2022 Oct;20(4):1105-1120. doi: 10.1007/s12021-022-09593-4. Epub 2022 Jun 22.
Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques.
全脑束追踪分割是研究大脑白质结构和连接模式的关键步骤。现有的基于监督分类的方法将束线分类为预定义的束线束类型，这些方法不适合探索子束结构，而基于人工设计特征的方法计算束线相似度的代价很高。为了解决这些问题，我们提出了一种新的无图谱方法，该方法使用无监督方式训练的深度递归自动编码器学习潜在空间。该方法有效地将任意长度的束线嵌入到固定大小的特征向量中，称为束线嵌入，用于在潜在空间中使用非参数聚类进行束追踪分割。该方法在 ISMRM 2015 追踪挑战赛数据集上进行了评估，使用聚类算法对主要束进行区分，并基于相似性进行束线查询，以及对 102 个人类连接组计划的真实束追踪进行了评估。所学习的潜在束线和束线表示为使用通用数据挖掘技术研究任意子束结构粒度的定量研究开辟了可能性。
