论文标题
与半神经网络的CT图像中的心外膜脂肪组织分割
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network
论文作者
论文摘要
心外膜脂肪组织是一种位于心脏壁之间的脂肪组织，心脏周围的保护层被称为心包。心外膜脂肪组织的体积和厚度与各种心血管疾病有关。它被证明是独立的心血管疾病危险因素。来自CT扫描的心外膜脂肪组织的全自动和可靠的测量可以提供更好的疾病风险评估，并能够为全身性心外膜脂肪组织研究处理大型CT图像数据集。本文提出了一种使用深神经网络从CT图像中对心外膜脂肪组织进行全自动语义分割的方法。拟议的网络使用基于U-NET的结构，其中嵌入了输入图像中的切片深度信息，以分割感兴趣的心包区域，该区域用于获得心外膜脂肪组织分割。图像增强用于增加模型鲁棒性。所提出方法的交叉验证在20名患者的CT扫描中的骰子得分为0.86。
Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to various cardiovascular diseases. It is shown to be an independent cardiovascular disease risk factor. Fully automatic and reliable measurements of epicardial adipose tissue from CT scans could provide better disease risk assessment and enable the processing of large CT image data sets for a systemic epicardial adipose tissue study. This paper proposes a method for fully automatic semantic segmentation of epicardial adipose tissue from CT images using a deep neural network. The proposed network uses a U-Net-based architecture with slice depth information embedded in the input image to segment a pericardium region of interest, which is used to obtain an epicardial adipose tissue segmentation. Image augmentation is used to increase model robustness. Cross-validation of the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.
