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.

翻译：白质高信号（WMH）仍是神经退行性疾病最重要的影像学生物标志物。稳健准确的WMH分割对神经影像学研究具有关键意义。随着MRI设备从3T向7T的逐步升级，亟需开发能够跨磁场强度及伪影实现统一分割的稳健工具。当前深度学习模型在WMH分割中展现出潜力，但仍面临训练数据多样性不足及MRI伪影影响分析有限等挑战。为此，我们提出wmh_seg——一种基于SegFormer的Transformer编码器的创新深度学习模型。该模型采用包含不同来源的1.5T、3T及7T FLAIR图像（并人工添加MR伪影）的独特训练数据集。我们的方法弥补了训练多样性与伪影分析方面的空白。实验表明，该模型在不同磁场强度、扫描仪厂商及常见MR成像伪影下均保持稳定性能。尽管超高场MR图像存在独特的非均匀性伪影，我们的模型仍能在7T FLAIR图像上实现稳健稳定的分割。截至目前，该模型是首个能够在7T FLAIR图像上实现高质量白质病变分割的方法。