弥散磁共振成像道路定向技术是一种先进的成像技术，能够体内映射脑的白质连接。白质分割将道路定向线束分类为群集或解剖有意义的径束。它能够量化和可视化全脑道路定向。目前，大多数分割方法侧重于深白质(DWM)，而较少的方法解决浅白质(SWM)分割的复杂性问题。我们提出了一种新颖的基于深度学习的两阶段框架——浅表白质分析(SupWMA)，它能够有效且一致地对全脑道路定向中的198个SWM聚类进行分割。我们将基于点云的网络适应于我们的SWM分割任务，并通过监督对比学习，增强各种合理线条和异常线条之间的区分度。我们在一个大规模的道路定向数据集上对模型进行训练，该数据集包括标记的长、中程(超过40mm)SWM聚类中的线条样本和解剖不合理的线条样本，并在六个独立获取的不同年龄和健康状况的数据集上进行测试(包括新生儿和患有占位性脑瘤的病人)。与几种最先进的方法相比，SupWMA在所有数据集上都获得了高度一致和准确的SWM分割结果，展示了对健康和疾病全生命周期的良好泛化能力。此外，SupWMA的计算速度比其他方法快得多。版权所有 © 2023 Elsevier B.V. 保留所有权利。

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.Copyright © 2023 Elsevier B.V. All rights reserved.