Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia.
The University of Sydney, NSW, Australia.
Neuroimage. 2023 Jun;273:120086. doi: 10.1016/j.neuroimage.2023.120086. Epub 2023 Apr 3.
White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.
脑白质纤维聚类是脑白质分割的重要策略，可实现健康和疾病状态下脑连接的定量分析。结合专家神经解剖学标记，数据驱动的脑白质纤维聚类是创建可跨个体模拟脑白质解剖结构图谱的强大工具。虽然广泛使用的纤维聚类方法已显示出使用经典无监督机器学习技术的良好性能，但深度学习的最新进展为快速有效的纤维聚类提供了一个很有前景的方向。在这项工作中，我们提出了一种新的脑白质纤维聚类深度学习框架 Deep Fiber Clustering (DFC)，该框架将无监督聚类问题作为一个自监督学习任务来解决，使用特定领域的预备任务来预测纤维对之间的距离。这个过程为每条纤维学习一个高维嵌入特征表示，而无需考虑追踪重建过程中纤维点的顺序。我们设计了一种新的网络架构，将输入纤维表示为点云，并允许从灰质分割中合并其他输入信息源。因此，DFC 利用了白质纤维几何形状和灰质解剖结构的综合信息来提高纤维聚类的解剖一致性。此外，DFC 通过拒绝聚类分配概率低的纤维，自然地进行异常值去除。我们在三个独立采集的队列上评估了 DFC，包括来自 220 名不同性别、年龄（年轻人和老年人）和不同健康状况（健康对照和多种神经精神障碍）个体的数据。我们将 DFC 与几种最先进的脑白质纤维聚类算法进行了比较。实验结果表明，DFC 在聚类紧致度、泛化能力、解剖一致性和计算效率方面具有优越的性能。
