Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Faculty of Mathematics, University of Vienna, Wien, Austria.
Neuroimage. 2021 Jun;233:117934. doi: 10.1016/j.neuroimage.2021.117934. Epub 2021 Mar 16.
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.
从弥散磁共振成像（dMRI）中分割脑组织类型是一项重要任务，需要对脑微结构进行定量分析，并提高轨迹追踪的准确性。目前的 dMRI 分割主要基于解剖磁共振成像（例如 T1 和 T2 加权）分割，这些分割与 dMRI 空间配准。然而，由于 dMRI 与解剖 MRI 相比存在更多的图像扭曲和更低的图像分辨率，因此这种模态间配准具有挑战性。在这项研究中，我们提出了一种用于弥散磁共振成像分割的深度学习方法，我们称之为 DDSeg。我们提出的方法从人类连接组计划（HCP）中的高质量成像数据中学习组织分割，其中解剖磁共振成像与 dMRI 的配准更加精确。然后，该方法能够直接从新的 dMRI 数据预测组织分割，包括使用不同采集协议收集的数据，而无需解剖数据和模态间配准。我们使用一种新的增强目标损失函数训练卷积神经网络（CNN），以学习组织分割模型，该函数旨在提高组织边界区域的准确性。为了进一步提高准确性，我们的方法添加了扩散峰度成像（DKI）参数，这些参数可描述非高斯水分子扩散，而不是传统的扩散张量成像参数。DKI 参数是从最近提出的平均峰度曲线方法中计算出来的，该方法纠正了不合理的 DKI 参数值，并提供了可区分组织类型的附加特征。我们在 HCP 数据上展示了较高的组织分割准确性，并且在将 HCP 训练的模型应用于具有较低分辨率和较少梯度方向的其他采集的 dMRI 数据时也表现出较高的准确性。
