AUTHOR=Gong Tan , Han Hualu , Tan Zheng , Ning Zihan , Qiao Huiyu , Yu Miaoxin , Zhao Xihai , Tang Xiaoying , Liu Gaifen , Shang Fei , Liu Shuai 

TITLE=Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1021477

DOI=10.3389/fneur.2022.1021477

ISSN=1664-2295

ABSTRACT=Background: White matter hyperintensities (WMHs) are a subtype of cerebral small vessel disease and can be divided into periventricular WMHs (pvWMHs) and deep WMHs (dWMHs). pvWMHs and dWMHs were proved to be determined by different etiologies. This study aimed to develop a 2D Cascade U-net (Cascade U) for segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images.
Methods: A total of 253 subjects were recruited in the present study. All subjects underwent 2D T2-FLAIR scan on a 3.0 Tesla MR scanner. Both contours of pvWMHs and dWMHs were manually delineated by the observers and considered as the gold standard. Fazekas scale was used to evaluate the burdens of pvWMHs and dWMHs, respectively. Cascade U consisted of a segmentation U-net and a differentiation U-net, and was trained with a combined loss function. The performance of Cascade U was compared with two other U-net (Pipeline U and Separate U). Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), precision and recall were used to evaluate the performances of all models. The linear correlations between WMHs volume (WMHV) measured by all models and the gold standard were also conducted.
Results:Compared with other models, Cascade U exhibited a better performance on WMHs segmentation as well as pvWMHs identification. Cascade U achieved DSC values of 0.605 ± 0.135, 0.517 ± 0.263 and 0.510 ± 0.241 and MCC values of 0.617 ± 0.122, 0.526 ± 0.263 and 0.522 ± 0.243 on segmentation of total WMHs, pvWMHs and dWMHs, respectively. Cascade U model exhibited strong correlations with the gold standard on measuring WMHV (R2 = 0.954, p < 0.001), pvWMHV (R2 = 0.933, p < 0.001) and dWMHV (R2 = 0.918, p < 0.001). A significant correlation was found between segmentation results and lesion volume (r > 0.510, p < 0.001).
Conclusion: Cascade U showed competitive results in segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images, indicating potential feasibility in precisely evaluating the burdens of WMHs.