Deep learning based segmentation of brain tissue from diffusion MRI

作者：Fan Zhang, Anna Breger, Kang Ik Kevin Cho, Lipeng Ning, Carl‐Fredrik Westin, Lauren J. O’Donnell, Ofer Pasternak · 发表于：NeuroImage · 年份：2021 · DOI：10.1016/j.neuroimage.2021.117934 · 被引用次数：77 · 研究领域：Advanced Neuroimaging Techniques and Applications、MRI in cancer diagnosis、Advanced MRI Techniques and Applications

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 prov...