Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.

中文翻译：

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Anat-SFSeg：利用点云深度学习进行解剖学引导的浅表纤维分割


扩散磁共振成像 (dMRI) 纤维束成像是绘制大脑结构连接图的关键技术。白质的准确分割，特别是浅层白质（SWM），对于神经科学和临床研究至关重要。然而，由于 U 形图案中相邻回旋连接较短，因此分割 SWM 具有挑战性。在这项工作中，我们提出了一种解剖学引导的浅层纤维分割（Anat-SFSeg）框架来提高 SWM 分割的性能。该框架由独特的纤维解剖描述符（名为 FiberAnatMap）和基于点云数据的深度学习网络组成。以点云表示的纤维的空间坐标以及个体和群体层面的解剖特征被输入到神经网络中。该网络在人类连接组计划 (HCP) 数据集上进行训练，并在具有一系列认知障碍水平的受试者上进行测试。一种名为纤维解剖区域比例（FARP）的新指标量化了定义的大脑区域中的纤维比例，并能够与其他方法进行比较。另一个指标称为解剖区域纤维计数（ARFC），表示每个簇中的平均纤维数量，用于评估受试者间差异。实验结果表明，Anat-SFSeg 在 HCP 数据集上实现了最高的准确性，并在临床数据集上表现出很好的泛化能力。弥散张量指标和 ARFC 显示阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 患者的疾病严重程度相关的变化。与认知等级的相关性表明，这些指标是 AD 的潜在神经影像生物标志物。 此外，Anat-SFSeg 可用于探索其他神经退行性疾病、神经发育或精神疾病。