Zhang Fan, Cetin Karayumak Suheyla, Hoffmann Nico, Rathi Yogesh, Golby Alexandra J, O'Donnell Lauren J
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Med Image Anal. 2020 Oct;65:101761. doi: 10.1016/j.media.2020.101761. Epub 2020 Jun 24.
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
白质纤维束分割，即识别属于具有解剖学意义的纤维束的纤维束成像纤维（流线轨迹），是实现纤维束量化和可视化的关键步骤。在本研究中，我们提出了一种深度学习纤维束分割方法（DeepWMA），该方法能够快速且一致地从全脑中识别出54条主要的深部白质纤维束。我们创建了一个包含100万个标记纤维样本的大规模训练纤维束成像数据集，并提出了一种新颖的二维多通道特征描述符（FiberMap），它对沿每条纤维的点的空间坐标进行编码。我们基于FiberMap学习了一个卷积神经网络（CNN）纤维分类模型，并在带有真实纤维标签的训练纤维束成像数据上获得了90.99%的高纤维分类准确率。然后，该方法在一个测试数据集上进行评估，该数据集包含来自六个独立采集的不同性别、不同年龄段（1天至82岁）以及不同健康状况（健康对照、神经精神疾病和脑肿瘤患者）的597例扩散MRI扫描。我们与两种最先进的纤维束分割方法进行了比较。实验结果表明，我们的方法获得了高度一致的纤维束分割结果，在所研究的所有受试者中，平均超过99%的纤维束被成功识别，最重要的是，包括新生儿和患有占位性脑肿瘤的患者。我们还证明了该方法对来自多种不同纤维追踪方法的纤维束成像数据具有良好的泛化能力。所提出的方法利用了深度学习技术，为大型扩散MRI纤维束成像数据集中的脑白质分割提供了一种快速有效的工具。
