This study deals with the task of segmentation of SEM images of fine ceramics sintered bodies by using deep neural network (DNN). In particular, we focus on misclassification caused by the blurriness of grain boundaries(boundaries between particles). Therefore, we utilize the frequency distribution of brightness gradient of grain boundaries and give higher weights to pixels with lower gradient values. Experiments confirmed that the model trained with proposed loss function gave the best prediction results.

中文翻译：

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用于图像分割的深度神经网络 (DNN) 模糊边界的损失函数

本研究涉及使用深度神经网络（DNN）对精细陶瓷烧结体的 SEM 图像进行分割的任务。我们特别关注由晶界（颗粒之间的边界）模糊引起的错误分类。因此，我们利用晶界亮度梯度的频率分布，对梯度值较低的像素赋予较高的权重。实验证实，用所提出的损失函数训练的模型给出了最好的预测结果。