Abstract
Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50–80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.
Highlights
Diffuse white matter abnormality (DWMA) is observed in 50–80% of very preterm infants at term-equivalent age (Maalouf et al, 1999; Skiöld et al, 2010; Parikh et al, 2013)
The only DWMA imaging-pathologic correlation study reported some histopathologic overlap with periventricular leukomalacia, and reported distinctive features, suggesting DWMA may be a form of diffuse white matter gliosis without microscopic necrosis (Parikh et al, 2016)
The signal-to-noise ratio (SNR), defined as the mean cerebral tissue intensity divided by noise standard deviation (SD), of the synthesized brain images was 22.5
Summary
Diffuse white matter abnormality (DWMA) is observed in 50–80% of very preterm infants at term-equivalent age (Maalouf et al, 1999; Skiöld et al, 2010; Parikh et al, 2013). Despite the well-documented presence of DWMA and emerging evidence of its pathological nature, the significance of DWMA for long-term neurodevelopment remains debatable (Dyet et al, 2006; Krishnan et al, 2007; Hart et al, 2010b; de Bruïne et al, 2011; Iwata et al, 2012; Jeon et al, 2012; He and Parikh, 2013a; Parikh et al, 2016; Volpe, 2017) Much of this debate has been fueled by the nearly universal use of qualitative reporting of DWMA that is subjective and unreliable, likely resulting in measurement error and lack of association with neurodevelopmental impairments in some studies (Hagmann et al, 2009; Hart et al, 2010a; de Bruïne et al, 2011). The only DWMA imaging-pathologic correlation study reported some histopathologic overlap with periventricular leukomalacia, and reported distinctive features, suggesting DWMA may be a form of diffuse white matter gliosis without microscopic necrosis (Parikh et al, 2016)
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