wmh_seg: Transformer based U-Net for Robust and Automatic White Matter
  Hyperintensity Segmentation across 1.5T, 3T and 7T
Jinghang Li Tales Santini Yuanzhe Huang Joseph M. Mettenburg Tamer S. Ibrahim Howard J. Aizenstein Minjie Wu
White matter hyperintensity (WMH) remains the top imaging biomarker for
neurodegenerative diseases. Robust and accurate segmentation of WMH holds
paramount significance for neuroimaging studies. The growing shift from 3T to
7T MRI necessitates robust tools for harmonized segmentation across field
strengths and artifacts. Recent deep learning models exhibit promise in WMH
segmentation but still face challenges, including diverse training data
representation and limited analysis of MRI artifacts' impact. To address these,
we introduce wmh_seg, a novel deep learning model leveraging a
transformer-based encoder from SegFormer. wmh_seg is trained on an unmatched
dataset, including 1.5T, 3T, and 7T FLAIR images from various sources,
alongside with artificially added MR artifacts. Our approach bridges gaps in
training diversity and artifact analysis. Our model demonstrated stable
performance across magnetic field strengths, scanner manufacturers, and common
MR imaging artifacts. Despite the unique inhomogeneity artifacts on ultra-high
field MR images, our model still offers robust and stable segmentation on 7T
FLAIR images. Our model, to date, is the first that offers quality white matter
lesion segmentation on 7T FLAIR images.