Abstract：
OBJECTIVE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.
METHODS: Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model\'s performance was evaluated using Lin\'s concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement.
RESULTS: A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%.
CONCLUSIONS: Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements.
METHODS: Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model\'s performance was evaluated using Lin\'s concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement.
RESULTS: A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%.
CONCLUSIONS: Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements.
摘要：
目的：本研究的目的是从平衡放射性核素血管造影数据集生成基于深度学习的感兴趣区域（ROI），用于左心室射血分数（LVEF）测量。
方法：从图像存档和通信系统的报告中提取收缩末期和舒张末期图像上的手动绘制ROI（mROI）。为了减少观察者的可变性，使用提取的mROI的最大像素数的41%阈值描绘预处理的ROI(pROI),并标记为地面实况。背景ROI是使用算法自动创建的,以识别在收缩末期ROI周围的指定概率区域内具有最小计数的区域。训练2维U-Net卷积神经网络架构以从pROI生成基于深度学习的ROI(dlROI)。使用Lin的一致性相关系数(CCC)评估模型的性能。Bland-Altman地块用于评估偏见和95％的一致性限制。
结果：共纳入41,462次扫描（19,309例患者）。dlROIs和pROIs的LVEF测量结果具有很强的一致性(CCC=85.6%；95%置信区间，85.4%-85.9%），以及来自dlROI和mROI的LVEF测量值之间(CCC=86.1%；95%置信区间，85.8%-86.3%）。在Bland-Altman分析中,LVEF测量的平均差异和95%的一致性界限分别为-0.6%和-6.6%至5.3%，分别,对于dlROI和pROI，dlROI和mROI分别为-0.4%和-6.3%至5.4%，分别。在37,537次扫描(91%)中，dlROIs和mROIs之间的绝对LVEF差异<5%。
结论：我们的2维U-Net卷积神经网络架构在从平衡放射性核素血管造影扫描生成LVROI方面表现出优异的性能。它可以增强LVEF测量的便利性和再现性。
方法：从图像存档和通信系统的报告中提取收缩末期和舒张末期图像上的手动绘制ROI（mROI）。为了减少观察者的可变性，使用提取的mROI的最大像素数的41%阈值描绘预处理的ROI(pROI),并标记为地面实况。背景ROI是使用算法自动创建的,以识别在收缩末期ROI周围的指定概率区域内具有最小计数的区域。训练2维U-Net卷积神经网络架构以从pROI生成基于深度学习的ROI(dlROI)。使用Lin的一致性相关系数(CCC)评估模型的性能。Bland-Altman地块用于评估偏见和95％的一致性限制。
结果：共纳入41,462次扫描（19,309例患者）。dlROIs和pROIs的LVEF测量结果具有很强的一致性(CCC=85.6%；95%置信区间，85.4%-85.9%），以及来自dlROI和mROI的LVEF测量值之间(CCC=86.1%；95%置信区间，85.8%-86.3%）。在Bland-Altman分析中,LVEF测量的平均差异和95%的一致性界限分别为-0.6%和-6.6%至5.3%，分别,对于dlROI和pROI，dlROI和mROI分别为-0.4%和-6.3%至5.4%，分别。在37,537次扫描(91%)中，dlROIs和mROIs之间的绝对LVEF差异<5%。
结论：我们的2维U-Net卷积神经网络架构在从平衡放射性核素血管造影扫描生成LVROI方面表现出优异的性能。它可以增强LVEF测量的便利性和再现性。
