论文标题
PAT-CNN：从T2加权心脏磁共振图像对心包脂肪组织的自动分割和定量
PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images
论文作者
论文摘要
背景：增加心包脂肪组织（PAT）与多种类型的心血管疾病（CVD）有关。尽管心脏磁共振图像（CMRI）通常是在CVD患者中获得的，但目前尚无工具可以自动识别和量化CMRI的PAT。这项研究的目的是建立一个神经网络，以分割T2加权CMRI的PAT，并探索PAT量（PATV）与CVD结果与死亡率之间的相关性。方法：我们培训并测试了一个深度学习模型PAT-CNN，以对T2加权心脏MR图像进行PAT分段。使用PAT-CNN的分割，我们自动计算了来自391名患者的图像的PATV。我们分析了PATV与CVD诊断与1年死亡率之间的相关性。结果：PAT-CNN能够准确地分割骰子得分/Hausdorff距离为0.74 +-0.03/27.1 +-10.9〜mm的PAT，类似于比较两个独立人类观察者的分割时获得的值（$ 0.76 +-0.76 +-0.06 +-0.06 +-0.06/21.2 +-10.3〜mm $）。回归模型表明，与性别和身体质量指数无关，PATV与CVD的诊断显着相关，并且与1年的诊断都导致死亡率（P值<0.01）。结论：PAT-CNN可以自动准确地从T2加权CMR图像中进行PAT。从CMRI自动测量的PATV增加与CVD的存在显着相关，并且可以独立预测1年的死亡率。
Background: Increased pericardial adipose tissue (PAT) is associated with many types of cardiovascular disease (CVD). Although cardiac magnetic resonance images (CMRI) are often acquired in patients with CVD, there are currently no tools to automatically identify and quantify PAT from CMRI. The aim of this study was to create a neural network to segment PAT from T2-weighted CMRI and explore the correlations between PAT volumes (PATV) and CVD outcomes and mortality. Methods: We trained and tested a deep learning model, PAT-CNN, to segment PAT on T2-weighted cardiac MR images. Using the segmentations from PAT-CNN, we automatically calculated PATV on images from 391 patients. We analysed correlations between PATV and CVD diagnosis and 1-year mortality post-imaging. Results: PAT-CNN was able to accurately segment PAT with Dice score/ Hausdorff distances of 0.74 +- 0.03/27.1 +- 10.9~mm, similar to the values obtained when comparing the segmentations of two independent human observers ($0.76 +- 0.06/21.2 +- 10.3~mm$). Regression models showed that, independently of sex and body-mass index, PATV is significantly positively correlated with a diagnosis of CVD and with 1-year all cause mortality (p-value < 0.01). Conclusions: PAT-CNN can segment PAT from T2-weighted CMR images automatically and accurately. Increased PATV as measured automatically from CMRI is significantly associated with the presence of CVD and can independently predict 1-year mortality.
