Identification of White Matter Lesions in Patients With Acute Ischemic Lesions Using U-net.Background: White matter lesions (WML) have been proved to be significantly associated with many brain diseases.  This study aimed to evaluate the performance of U-net in evaluation of WML in the patients with WML and AIL.  The performance of lesion segmentation toolbox (LST) and three U-net models were evaluated on three levels: pixel, lesion, and subject levels.  Results: All U-net models outperformed LST on pixel, lesion, and subject levels, while no differences were found among three U-net models.  The scores determined by U-net exhibited a strong correlation with the gold standard (all Spearman correlation coefficients >0.89, ICCs >0.88, p-values <0.001).  Conclusion: U-net performs well on identification and segmentation of WML in the patients with WML and AIL.  The performance of U-net is validated by a dataset of multicenter study.  Our results indicate that U-net has an advantage in assessing the burden of WML in the patients suffered from both WML and AIL.
U-net