Cai Die, Pan Minmin, Liu Chenyuan, He Wenjie, Ge Xinting, Lin Jiaying, Li Rui, Liu Mengting, Xia Jun
Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
School of Information Science and Engineering, Shandong Normal University, Shandong, China.
Front Aging Neurosci. 2024 Aug 29;16:1457405. doi: 10.3389/fnagi.2024.1457405. eCollection 2024.
Studying perivascular spaces (PVSs) is important for understanding the pathogenesis and pathological changes of neurological disorders. Although some methods for automated segmentation of PVSs have been proposed, most of them were based on 7T MR images that were majorly acquired in healthy young people. Notably, 7T MR imaging is rarely used in clinical practice. Herein, we propose a deep-learning-based method that enables automatic segmentation of PVSs on T2-weighted 3T MR images.
Twenty patients with Parkinson's disease (age range, 42-79 years) participated in this study. Specifically, we introduced a multi-scale supervised dense nested attention network designed to segment the PVSs. This model fosters progressive interactions between high-level and low-level features. Simultaneously, it utilizes multi-scale foreground content for deep supervision, aiding in refining segmentation results at various levels.
Our method achieved the best segmentation results compared with the four other deep-learning-based methods, achieving a dice similarity coefficient (DSC) of 0.702. The results of the visual count of the PVSs in our model correlated extremely well with the expert scoring results on the T2-weighted images (basal ganglia: rs = 0.845, < 0.001; rs = 0.868, < 0.001; centrum semiovale: rs = 0.845, < 0.001; rs = 0.823, < 0.001 for raters 1 and 2, respectively). Experimental results show that the proposed method performs well in the segmentation of PVSs.
The proposed method can accurately segment PVSs; it will facilitate practical clinical applications and is expected to replace the method of visual counting directly on T1-weighted images or T2-weighted images.
研究血管周围间隙（PVSs）对于理解神经疾病的发病机制和病理变化具有重要意义。尽管已经提出了一些自动分割PVSs的方法，但大多数方法基于主要在健康年轻人中采集的7T磁共振（MR）图像。值得注意的是，7T MR成像在临床实践中很少使用。在此，我们提出一种基于深度学习的方法，能够在T2加权3T MR图像上自动分割PVSs。
20例帕金森病患者（年龄范围42 - 79岁）参与了本研究。具体而言，我们引入了一种多尺度监督密集嵌套注意力网络来分割PVSs。该模型促进了高级和低级特征之间的渐进交互。同时，它利用多尺度前景内容进行深度监督，有助于在各个级别细化分割结果。
与其他四种基于深度学习的方法相比，我们的方法取得了最佳分割结果，骰子相似系数（DSC）达到0.702。我们模型中PVSs的视觉计数结果与T2加权图像上的专家评分结果高度相关（基底节：评分者1和评分者2的相关系数rs分别为0.845，P < 0.001；rs = 0.868，P < 0.001；半卵圆中心：rs = 0.845，P < 0.001；rs = 0.823，P < 0.001）。实验结果表明，所提出的方法在PVSs分割中表现良好。
所提出的方法能够准确分割PVSs；它将有助于实际临床应用，并有望取代直接在T1加权图像或T2加权图像上进行视觉计数的方法。
