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.

使用 wmh_seg 这一基于转换器编码器的深度学习模型，该模型在不同磁场强度、扫描仪制造商和常见磁共振成像伪影的条件下，稳定地实现了 7T 患者的白质病变分割。

wmh_seg: 转换器基于的 U-Net 用于 1.5T、3T 和 7T 的鲁棒和自动化的白质高信号区分割

wmh_seg: Transformer based U-Net for Robust and Automatic White Matter  Hyperintensity Segmentation across 1.5T, 3T and 7T

White Matter Hyperintensity (WMH) is an imaging feature related to various
diseases such as dementia and stroke. Accurately segmenting WMH using computer
technology is crucial for early disease diagnosis. However, this task remains
challenging due to the small lesions with low contrast and high discontinuity
in the images, which contain limited contextual and spatial information. To
address this challenge, we propose a deep learning model called 3D Spatial
Attention U-Net (3D SA-UNet) for automatic WMH segmentation using only Fluid
Attenuation Inversion Recovery (FLAIR) scans. The 3D SA-UNet introduces a 3D
Spatial Attention Module that highlights important lesion features, such as
WMH, while suppressing unimportant regions. Additionally, to capture features
at different scales, we extend the Atrous Spatial Pyramid Pooling (ASPP) module
to a 3D version, enhancing the segmentation performance of the network. We
evaluate our method on publicly available dataset and demonstrate the
effectiveness of 3D spatial attention module and 3D ASPP in WMH segmentation.
Through experimental results, it has been demonstrated that our proposed 3D
SA-UNet model achieves higher accuracy compared to other state-of-the-art 3D
convolutional neural networks.

使用 3D Spatial Attention U-Net（3D SA-UNet）模型对液体衰减逆转复苏（FLAIR）扫描进行自动白质高信号（WMH）分割，通过增加 3D 空间注意力模块和 3D ASPP 模块，提高了网络的分割性能，并实验证明了该模型相比其他先进的 3D 卷积神经网络有更高的准确性。

3D SA-UNet：3D 空间注意 UNet 与 3D ASPP 用于白质高信号区域分割

3D SA-UNet: 3D Spatial Attention UNet with 3D ASPP for White Matter  Hyperintensities Segmentation

White Matter Hyperintensities (WMH) are the most common manifestation of
cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH
segmentation algorithms are important to determine cSVD burden and its clinical
consequences. Most of existing WMH segmentation algorithms require both fluid
attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs.
However, T1-weighted images are typically not part of standard clinicalscans
which are acquired for patients with acute stroke. In this paper, we propose a
novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR
images with a spatially-registered white matter (WM) brain atlas to yield
competitive WMH segmentation performance. Specifically, we designed a dual-path
segmentation model with two novel connecting mechanisms, namely multi-input
attention module (MAM) and attention fusion module (AFM) to fuse the
information from two paths for accurate results. Experiments on two publicly
available datasets show the effectiveness of the proposed BAGAU-Net. With only
FLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-art
method with T1-weighted images, paving the way for effective development of WMH
segmentation. Availability:this https URL

本文提出了一种仅利用 FLAIR 图像和空间注册的白质（WM）脑图谱的脑图谱引导的关注 U-Net（BAGAU-Net）来产生竞争性的 WMH 分割性能，通过实验验证表明：BAGAU-Net 胜过了 T1 加持的最先进方法，为 WMH 分割的有效发展铺平了道路。