Abstract

White matter hyperintensity (WMH) is associated with various aging and neurodegenerative diseases. In this paper, we proposed and validated a fully automatic system which integrates classical image processing and deep neural network for segmenting WHM from fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) images. In this system, a novel skip connection U-net (SC U-net) was proposed. In addition, an atlas-based method was introduced in the preprocessing stage to remove non-brain tissues (namely skull-stripping) and thus to improve the segmentation accuracy. Effectiveness of the proposed system was validated on a dataset of 60 paired images based on cross-scanner validation. Our experimental results revealed the effectiveness of the skull-stripping strategy. More importantly, compared to two existing state-of-the-art methods for segmenting WHM, including a U-net-like method and another deep learning method, the proposed SC U-net had a faster convergence, a lower loss and a higher segmentation accuracy. Both quantitative and qualitative analyses (via visual examinations) revealed the superior performance of our proposed SC U-net. The mean dice score of the proposed SC U-net was 78.36% which was much higher than those of a U-net-like method (74.99%) and an alternative deep learning method (74.80%). The software environment and model of the proposed system were made publicly accessible at Dockerhub.

Highlights

  • White matter hyperintensities (WMH), known as leukoaraiosis, are brain areas of increased signal intensities, indicating macroscopic changes of brain tissues induced by white matter damages [1]

  • Since WMH are brighter than healthy brain tissues when revealed on fluid attenuation inversion recovery (FLAIR) images, many unsupervised methods used thresholding to segment WMH

  • A Bayesian Markov random field (MRF) relief was employed by Schwarz et al [22] using lognormal distributions for detection white matter and WMH.Wu et al [23] proposed a multi-atlas based method for simultaneously detecting and localizing WMH on FLAIR data

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Summary

Introduction

A. CLINICAL MOTIVATION WMH are characteristics of aging and neurodegenerative diseases targeting the white matter, including stroke, dementia and multiple sclerosis (MS) [2]–[5]. CLINICAL MOTIVATION WMH are characteristics of aging and neurodegenerative diseases targeting the white matter, including stroke, dementia and multiple sclerosis (MS) [2]–[5] These diseases may cause irreversible damages to the human brain [6], [7]. Etiologies of these diseases are not yet fully understood, there is considerable evidence suggesting that they are related to WMH [3], [8]. Trapp and colleagues concluded that MS is an immune-mediated demyelinating

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