论文标题
使用段CMR软件评估基于深度学习的心肌梗塞定量
Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software
论文作者
论文摘要
这项工作使用节段心血管磁共振（CMR）软件评估了基于深度学习的心肌梗塞（MI）定量。段CMR软件包含了预期最大化，加权强度，先验信息（EWA）算法，用于产生梗塞疤痕量，梗塞疤痕百分比和微血管障碍物百分比。在这里，通过使用U-NET进行语义细分，对段CMR软件细分算法进行了更新，以实现和评估完全自动化或深度学习的MI量化。直接观察图以及梗塞和轮廓心肌的数量是两个选项，用于估计基于深度学习的MI量化与基于医学专家的结果之间的关系。
This work evaluates deep learning-based myocardial infarction (MI) quantification using Segment cardiovascular magnetic resonance (CMR) software. Segment CMR software incorporates the expectation-maximization, weighted intensity, a priori information (EWA) algorithm used to generate the infarct scar volume, infarct scar percentage, and microvascular obstruction percentage. Here, Segment CMR software segmentation algorithm is updated with semantic segmentation with U-net to achieve and evaluate fully automated or deep learning-based MI quantification. The direct observation of graphs and the number of infarcted and contoured myocardium are two options used to estimate the relationship between deep learning-based MI quantification and medical expert-based results.
