论文标题
大脑图集引导着注意U-NET对白质超强度细分
Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation
论文作者
论文摘要
白质高强度（WMH）是脑MRI上脑小血管疾病（CSVD）最常见的表现。准确的WMH分割算法对于确定CSVD负担及其临床后果很重要。现有的大多数WMH分割算法都需要流体衰减的反转恢复（FLAIR）图像和T1加权图像作为输入。但是，T1加权图像通常不属于标准临床SCAN的一部分，这些临床SCAN是急性中风患者获得的。在本文中，我们提出了一个新型的大脑图书馆引起了注意U-NET（BAGAU-NET），该图像仅利用空间注册的白质（WM）脑图集以产生竞争性的WMH分割性能。具体而言，我们设计了一个双路段分割模型，它具有两个新型的连接机制，即多输入注意模块（MAM）和注意融合模块（AFM），以融合来自两个路径的信息以获得准确的结果。两个公开可用数据集的实验显示了拟议的Bagau-net的有效性。只有Flair图像和WM Brain Atlas，Bagau-net的表现优于最先进的方法，其中具有T1加权图像，为有效开发WMH分割铺平了道路。可用性：https：//github.com/ericzhang1/bagau-net
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:https://github.com/Ericzhang1/BAGAU-Net
