Heterogeneous catalysts possess complex surface and bulk structures, relatively poor intrinsic contrast, and often a sparse distribution of the catalytic nanoparticles (NPs), posing a significant challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a $\gamma$-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net's fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 $\pm$ 0.003 in the $\gamma$-Alumina support material and 0.84 $\pm$ 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for $\gamma$-Alumina and Pt NPs segmentations. The complex surface morphology of the $\gamma$-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

翻译：为解决这一问题,我们采用深层次的学习方法,在舱面和体积结构复杂,内部对比相对较差,而且催化纳米粒子(NPs)分布稀少,对图像解析构成重大挑战,包括当前最先进的深层学习方法。为解决这一问题,我们采用基于深层次学习的方法,在舱面失衡的情况下,对美元/光素/Pt催化材料进行多级解析分解。具体地说,我们将加权焦量损失用作损失函数,并将其附在U-Net的完全 convolual网络结构中。我们利用了数字流缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩略图(调缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩略图),我们采用的U-Net模型平均得分数为0.96美元/003美元。