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.
■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。