Dilated Saliency U-Net for White Matter Hyperintensities Segmentation using Irregularity Age Map

Abstract

2White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity 3 on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been 4 noteworthy in age-related neuroscience for being a crucial biomarker for Alzheimer's disease 5 and brain aging processes. However, the automatic WMH segmentation is challenging because 6 of the variable intensity range, size and shape. U-Net tackled this problem through the dense 7 prediction and showed competitive performances on not only WMH segmentation/detection but 8 also on varied image segmentation tasks, but it still accompanies a high complexity of the network 9 architecture. In this study, we propose to use Saliency U-Net architecture and irregularity age 10 map(IAM) to decrease the U-Net complexity without a performance loss. We trained Saliency 11 U-Net using both T2-FLAIR MRI sequence and IAM. Since IAM guides where irregularities, in 12 which WMH is possibly included, exist on the MRI slice, Saliency U-Net performs better than the 13 original U-Net trained only using T2-FLAIR. The better performance was achieved with fewer 14 parameters and shorter training time. Moreover, the application of dilated convolution enhanced 15 Saliency U-Net to recognise the shape of large WMH more accurately by learning multi-context 16 on MRI slices. This network named Dilated Saliency U-Net improved Dice coefficient score to 17 0.5588 which is the best score among our experimental models, and recorded a relatively good 18 sensitivity of 0.4747 with the shortest train time and the least number of parameters. In conclusion, 19 based on the experimental results, incorporating IAM through Dilated Saliency U-Net resulted an 20 appropriate approach for WMH segmentation. 21 22 25progression (Gootjes et al., 2004). Higher volume of WMH has been found in brains of AD patients 26 compared to age-matched controls, and the degree of WMH has been reported more severe for senile onset 27 AD patients than presenile onset AD patients (Scheltens et al., 1992). Furthermore, WMH volume generally 28 increases with the advance of age (Raz et al., 2012; Jagust et al., 2008). Due to their clinical importance, 29 various machine learning approaches have been implemented for the automatic WMH segmentation 30 (Admiraal-Behloul et al., 2005; Bowles et al., 2017).31 LOTS-IM algorithm is an unsupervised algorithm for detecting tissue irregularities, that successfully has 32 been applied for segmenting WMH regions on brain T2-FLAIR images (Rachmadi et al., 2019). Without 33 any ground-truth segmentation, this algorithm produces a map which describes how much each voxel is 34 irregular compared with an overall area. This map is usually called 'irregularity map' (IM) or 'irregularity 35 age map (IAM)'. The concept of this map was firstly suggested in the field of computer graphics to calculate 36 1 Yunhee Jeong et al. DSUNet WMH segmentation using IAM pixel-wise age values indicating how weathered/damaged each pixel is compared to the overall texture 37 pattern of an ...

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

“…IM is also independent from a human rater or training data, as it is produced using an unsupervised method (i.e., LOTS-IM) (Rachmadi et al, 2019b). Furthermore, previous studies have shown that IM can also be used for WMH segmentation (Rachmadi et al, 2018b), data augmentation of supervised WMH segmentation (Jeong et al, 2019), and simulation of WMH progression and regression (Rachmadi et al, 2018c). DEM resulted from the subtraction of two IMs has values ranging from -1 to 1 (first row of Figure 1).…”

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