Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants.

Abstract

About 50%-80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. This secondary analysis of prospective data was performed with an internal cohort of 98 VPIs (data collected from December 2014 to April 2016) and an external cohort of 28 VPIs (data collected from January 2012 to August 2014) who had already undergone MRI at term-equivalent age. Ground truth DWMA regions were manually annotated by two human experts with the guidance of a prior published semiautomated algorithm. In a twofold cross-validation experiment using the internal cohort of 98 infants, the three-dimensional (3D) ResU-Net model accurately segmented DWMA with a Dice similarity coefficient of 0.907 ± 0.041 (standard deviation) and balanced accuracy of 96.0% ± 2.1, outperforming multiple peer deep learning models. The 3D ResU-Net model that was trained with the whole internal cohort (n = 98) was further tested on an independent external test cohort (n = 28) and achieved a Dice similarity coefficient of 0.877 ± 0.059 and balanced accuracy of 92.3% ± 3.9. The externally validated 3D ResU-Net deep learning model for accurately segmenting DWMA may facilitate the clinical diagnosis of DWMA in VPIs. Supplemental material is available for this article. Keywords: Brain/Brain Stem, Convolutional Neural Network (CNN), MR-Imaging, Pediatrics, Segmentation, Supervised learning © RSNA, 2021.