The best performance was achieved with fewer parameters and shorter training time. Since IAM guides locating image intensity irregularities, in which WMH are possibly included, in the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. We trained Saliency U-Net using both: a T2-FLAIR MRI sequence and its correspondent IAM. In this study, we propose the use of Saliency U-Net and Irregularity map (IAM) to decrease the U-Net architectural complexity without performance loss. However, its network architecture is high complex.

U-Net tackles this problem through the dense prediction and has shown competitive performances not only on WMH segmentation/detection but also on varied image segmentation tasks. The automatic WMH segmentation is challenging because of their variable intensity range, size and shape. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for all types of dementia and brain aging processes. White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences.

2Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.

1School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

Yunhee Jeong 1 Muhammad Febrian Rachmadi 1,2 Maria del C.

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