PDF(Pubmed)
Abstract：
UNASSIGNED: Recent studies have shown that epicardial adipose tissue (EAT) is an independent atrial fibrillation (AF) prognostic marker and has influence on the myocardial function. In computed tomography (CT), EAT volume (EATv) and density (EATd) are parameters that are often used to quantify EAT. While increased EATv has been found to correlate with the prevalence and the recurrence of AF after ablation therapy, higher EATd correlates with inflammation due to arrest of lipid maturation and with high risk of plaque presence and plaque progression. Automation of the quantification task diminishes the variability in readings introduced by different observers in manual quantification and results in high reproducibility of studies and less time-consuming analysis. Our objective is to develop a fully automated quantification of EATv and EATd using a deep learning (DL) framework.
UNASSIGNED: We proposed a framework that consists of image classification and segmentation DL models and performs the task of selecting images with EAT from all the CT images acquired for a patient, and the task of segmenting the EAT from the output images of the preceding task. EATv and EATd are estimated using the segmentation masks to define the region of interest. For our experiments, a 300-patient dataset was divided into two subsets, each consisting of 150 patients: Dataset 1 (41,979 CT slices) for training the DL models, and Dataset 2 (36,428 CT slices) for evaluating the quantification of EATv and EATd.
UNASSIGNED: The classification model achieved accuracies of 98% for precision, recall and F 1 scores, and the segmentation model achieved accuracies in terms of mean ( ± std.) and median dice similarity coefficient scores of 0.844 ( ± 0.19) and 0.84, respectively. Using the evaluation set (Dataset 2), our approach resulted in a Pearson correlation coefficient of 0.971 ( R 2 = 0.943) between the label and predicted EATv, and the correlation coefficient of 0.972 ( R 2 = 0.945) between the label and predicted EATd.
UNASSIGNED: We proposed a framework that provides a fast and robust strategy for accurate EAT segmentation, and volume (EATv) and attenuation (EATd) quantification tasks. The framework will be useful to clinicians and other practitioners for carrying out reproducible EAT quantification at patient level or for large cohorts and high-throughput projects.
UNASSIGNED: We proposed a framework that consists of image classification and segmentation DL models and performs the task of selecting images with EAT from all the CT images acquired for a patient, and the task of segmenting the EAT from the output images of the preceding task. EATv and EATd are estimated using the segmentation masks to define the region of interest. For our experiments, a 300-patient dataset was divided into two subsets, each consisting of 150 patients: Dataset 1 (41,979 CT slices) for training the DL models, and Dataset 2 (36,428 CT slices) for evaluating the quantification of EATv and EATd.
UNASSIGNED: The classification model achieved accuracies of 98% for precision, recall and F 1 scores, and the segmentation model achieved accuracies in terms of mean ( ± std.) and median dice similarity coefficient scores of 0.844 ( ± 0.19) and 0.84, respectively. Using the evaluation set (Dataset 2), our approach resulted in a Pearson correlation coefficient of 0.971 ( R 2 = 0.943) between the label and predicted EATv, and the correlation coefficient of 0.972 ( R 2 = 0.945) between the label and predicted EATd.
UNASSIGNED: We proposed a framework that provides a fast and robust strategy for accurate EAT segmentation, and volume (EATv) and attenuation (EATd) quantification tasks. The framework will be useful to clinicians and other practitioners for carrying out reproducible EAT quantification at patient level or for large cohorts and high-throughput projects.
摘要：
■最近的研究表明，心外膜脂肪组织（EAT）是独立的房颤（AF）预后标志物，对心肌功能有影响。在计算机断层扫描（CT）中，EAT体积(EATv)和密度(EATd)是经常用于量化EAT的参数。虽然已发现EATv升高与消融治疗后房颤的患病率和复发相关。较高的EATd与由于脂质成熟停滞引起的炎症以及斑块存在和斑块进展的高风险相关。量化任务的自动化减少了不同观察者在手动量化中引入的读数的可变性，并导致研究的高可重复性和耗时较少的分析。我们的目标是使用深度学习（DL）框架开发EATv和EATd的全自动量化。
■我们提出了一个框架，该框架由图像分类和分割DL模型组成，并执行从为患者采集的所有CT图像中选择EAT图像的任务，以及从上一个任务的输出图像中分割EAT的任务。使用分割掩模估计EATv和EATd以限定感兴趣区域。对于我们的实验，300名患者的数据集被分为两个子集，每个由150名患者组成：数据集1（41,979个CT切片），用于训练DL模型，和Dataset2（36,428CT切片）用于评估EATv和EATd的定量。
■分类模型的精度达到了98%，召回和F1得分，分割模型在平均值(±std。）和中值骰子相似系数得分分别为0.844（±0.19）和0.84。使用评估集（数据集2），我们的方法导致标签和预测的EATV之间的皮尔逊相关系数为0.971(R2=0.943)，标签与预测EATd的相关系数为0.972(R2=0.945)。
■我们提出了一个框架，该框架为准确的EAT细分提供了快速而强大的策略，和体积(EATv)和衰减(EATd)量化任务。该框架将对临床医生和其他从业人员有用，用于在患者水平上进行可重复的EAT量化或用于大型队列和高通量项目。
■我们提出了一个框架，该框架由图像分类和分割DL模型组成，并执行从为患者采集的所有CT图像中选择EAT图像的任务，以及从上一个任务的输出图像中分割EAT的任务。使用分割掩模估计EATv和EATd以限定感兴趣区域。对于我们的实验，300名患者的数据集被分为两个子集，每个由150名患者组成：数据集1（41,979个CT切片），用于训练DL模型，和Dataset2（36,428CT切片）用于评估EATv和EATd的定量。
■分类模型的精度达到了98%，召回和F1得分，分割模型在平均值(±std。）和中值骰子相似系数得分分别为0.844（±0.19）和0.84。使用评估集（数据集2），我们的方法导致标签和预测的EATV之间的皮尔逊相关系数为0.971(R2=0.943)，标签与预测EATd的相关系数为0.972(R2=0.945)。
■我们提出了一个框架，该框架为准确的EAT细分提供了快速而强大的策略，和体积(EATv)和衰减(EATd)量化任务。该框架将对临床医生和其他从业人员有用，用于在患者水平上进行可重复的EAT量化或用于大型队列和高通量项目。
