Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study - Zhu, Wenhao; Huang, Hao; Zhou, Yaqi; Shi, Feng; Shen, Hong; Chen, Ran; Hua, Rui; Wang, Wei; Xu, Shabei; Luo, Xiang (Frontiers in Aging Neuroscience, 2022)
White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset from 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed the solid basis for the development and evaluation of automatic segmentation system. The proposed algorithm outperformed other state-of-the-art methods (uResNet, 3D V-Net and VGGNet) on the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 Medical Image Computing and Computer Assisted Intervention (MICCAI) WMH Segmentation Challenge dataset (with thin-slice MR images), all showing great effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and was very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice.