In this paper the main objective is to determine the best size of late gadolinium enhancement (LGE)-magnetic resonance imaging (MRI) images for the training dataset to achieve optimal deep learning training outcomes. To determine the new size of LGE-MRI images from the reference training dataset, non-extra pixel and extra pixel interpolation algorithms are used. A novel strategy based on thresholding, median filtering, and subtraction operations is introduced and applied to remove extra class labels in interpolated ground truth (GT) segmentation masks. Fully automated quantification is achieved using the expectation maximization, weighted intensity, a priori information (EWA) algorithm, and the outcome of automatic semantic segmentation of LGE-MRI images with the convolutional neural network (CNN). In the experiments, common class metrics are used to evaluate the quality of semantic segmentation with a CNN architecture of interest (U-net) against the GT segmentation. Arbitrary threshold, comparison of the sums, and sums of differences are criteria or options used to estimate the relationship between semi-automatic and fully automated quantification of MI results. A close relationship between semi-automatic or manual and fully automated quantification of MI results was more identified in the case involving the dataset of bigger LGE MRI images than in that of the dataset of smaller LGE-MRI images where the best quantification results based on the dataset of bigger LGE MRI images were 55.5% closer the manual or semiautomatic results while the best quantification results based on the dataset of smaller LGE MRI images were 22.2% closer the manual results.

翻译：在本文中,主要目标是确定用于培训数据集的最优化深学习培训结果的培训数据集(MRI)延迟加分增强成像(LGE)磁共振成像(MRI)图像的最大尺寸,以便实现最佳深层学习培训结果; 确定参考培训数据集、非extra像素和额外的像素内插算法中LGE-MRI图像的新尺寸; 引入并应用基于阈值、中位过滤和减法操作的新战略,以去除更大调解的地面分解面(GT)中的额外类标签; 利用预期最大化、加权强度、前置信息(EWA)算法以及LGE-MRI图像与 convolutional 神经网络(CNN)的自动分解成像(LGE-M)图像的新大小; 采用普通类衡量标准,用以评估以CNNCEM(U-net)为基础的利益结构(U-net)的分解成像质量质量的质量; 任意门槛、对数值的比较和差异的比较是用来估计半数级图像的最大标准或选项,而精度(L-IMAL)的半自动和完全定量数据结果是更接近的L的半自动的ML的内测结果。