Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson’s r; analysis of covariance; Bland–Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90 min to 20 s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/R without/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

梗死面积 （IS） 是评估心脏保护临床前研究成功与否的最可靠终点。缺血/再灌注 （I/R） 实验中 IS 定量的金标准是三苯基四唑氯化物 （TTC） 染色，通常手动完成。本研究旨在确定通过深度学习分割实现自动化是否是标准 IS 量化的省时且有效的替代方案。回顾性收集来自 TTC 染色的肉眼可见心脏切片的高分辨率图像，这些图像来自猪实验 （n = 390），I/R 没有/有心脏保护，以覆盖广泛的 IS 范围。来自猪实验的现有 IS 数据，使用手动和随后的胶片扫描注释数字标记的标准方法进行量化，用作参考。为了自动化评估过程以更客观地节省时间，我们实施了深度学习管道;通过裁剪和标记（图像注释）对收集的图像 （n = 3869） 进行预处理。为了确保它们作为深度学习分割模型的训练数据的可用性，IS 从图像注释中量化，并与使用现有胶片扫描注释量化的 IS 进行比较。开发并训练了一种基于动态 U-Net 架构的监督式深度学习分割模型。通过五重交叉验证（n = 220 个实验）和在独立测试集上进行测试（n = 170 个实验）来评估训练后的模型。计算性能指标（Dice 相似系数 [DSC]、像素准确度 [ACC]、平均精度 [mAP]）。 然后从预测中量化 IS，并与从图像注释量化的 IS 进行比较（线性回归，Pearson 的 r;协方差分析;Bland-Altman 图）。接近 1 的性能指标表明模型在交叉验证数据（DSC：0.90，ACC：0.98，mAP：0.90）和测试集数据（DSC：0.89，ACC：0.98，mAP：0.93）上具有很强的性能。从预测中量化的 IS 与从所有数据集中的图像注释量化的 IS 密切相关（交叉验证：r = 0.98;测试数据集：r = 0.95），协方差分析发现没有显着差异。该模型将每个实验的 IS 定量时间从大约 90 分钟缩短到 20 秒。该模型在分离的、盐水灌注大鼠心脏的实验集上进行了进一步测试，该实验具有区域 I/R，无/有心脏保护 （n = 27）。图像注释和预测之间的 IS 也没有显著差异，但来自大鼠心脏的测试集数据的性能较低 （DSC： 0.66， ACC： 0.91， mAP： 0.65）。使用深度学习分割模型进行 IS 量化是手动和后续数字标记的有效且省时的替代方案。