White matter fiber clustering (WMFC) is an important strategy for white matter parcellation, which enables quantitative analysis of white matter connections in health and disease. WMFC is usually performed in an unsupervised manner without the need of labeled ground truth data. While widely used WMFC approaches have shown good performance using classical machine learning techniques, recent advances in deep learning reveal a promising direction towards fast and effective WMFC. In this work, we propose a novel deep learning framework for WMFC, 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 enables the fiber representation learning to handle a known challenge in WMFC, i.e., the sensitivity of clustering results to the point ordering along fibers. 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 anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal in a natural way 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 WMFC algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.
翻译:白质纤维聚合(WMFC)是白质包包化的一个重要战略,它有助于对健康和疾病中的白质连接进行定量分析。WMFC通常以不受监督的方式进行,不需要贴标签的地面真实数据。虽然广泛使用的WMFC方法使用经典机器学习技术表现出良好的表现,但最近深层次学习的进展显示,向快速有效的WMFC方向的方向有希望。在这项工作中,我们提议为WMFC、深纤维包化(DFC)提供一个全新的深层次学习框架,解决了未监督的集群问题,作为自我监督的学习任务,以具体领域为借口,预测对齐的纤维距离。这使得纤维代表学习能够应对WMFC中已知的挑战,也就是说,集群结果对纤维的敏感程度。 我们设计了一个新的网络结构,将输入纤维作为点云,并能够纳入灰质包化的输入信息的其他来源。 因此,DFC将白质纤维的测深和灰质解剖质的合并信息用于改善纤维内部结构的连贯性一致性。 在IMFC中,我们用高层次测算和高层次的递解析的机变变变的机变变变的机变的机变法, 以不同的机变法计算,用一种方法, 以不同的机变法计算法计算,以不同的机变法计算。