汉译英：为了训练深度学习模型并评估结果将1471幅图像分为训练集和验证集每次按照5倍交叉验证的方法Resch et al2021随机抽取1471幅中约五分之一的图像303幅图像组成验证集其余图像1168幅图像形成训练集。在实验过程中使用训练集中的图像进行训练并使用验证集中的图像来测试分割方法。该评估过程重复10次交替使用训练集和验证集每次计算评估指标。计算单个运行的平均值和标准偏差以生成最终结果。

To train the deep learning model and evaluate the results, 1471 images were divided into a training set and a validation set. For each iteration, approximately one-fifth of the images (303 images) were randomly selected from the 1471 images using a 5-fold cross-validation method (Resch et al., 2021) to form the validation set, while the remaining images (1168 images) formed the training set. During the experiment, the images in the training set were used for training, and the images in the validation set were used to test the segmentation method. This evaluation process was repeated 10 times, alternating between the training set and validation set, and the evaluation metrics were calculated each time. The average and standard deviation of each run were calculated to generate the final results.