In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for quantification of myocardial infarction (MI) in automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the sums, and sums of differences are methods used to estimate the relationship between semi-automatic or manual and fully automated quantification of myocardial infarction (MI) results. The relationship between semi-automatic and fully automated quantification of MI results was found to be closer in the case of bigger LGE MRI images (55.5% closer to manual results) than in the case of smaller LGE MRI images (22.2% closer to manual results).

翻译：本研究确定了训练数据集中晚期钆增强（LGE）磁共振成像（MRI）图像的最佳尺寸，以优化深度学习训练结果。采用非额外像素与额外像素插值算法来确定LGE-MRI图像的新尺寸。引入了一种新策略来处理插值掩模，并移除插值真值（GT）分割掩模中的多余类别标签。使用期望最大化、加权强度与先验信息（EWA）算法对自动分割的LGE-MRI图像中的心肌梗死（MI）进行量化。任意阈值、总和比较与差值总和等方法用于评估半自动或手动与全自动心肌梗死（MI）量化结果之间的关系。结果表明，较大尺寸的LGE-MRI图像（与手动结果接近55.5%）中半自动与全自动MI量化结果之间的关系比小尺寸图像（与手动结果接近22.2%）更紧密。