一种深度学习方法用于催化材料电子断层扫描中不平衡数据的语义分割
A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials
摘要：
非均相催化剂具有复杂的表面和体相结构、相对较差的内在对比度，并且催化纳米颗粒（NP）通常分布稀疏，这对图像分割（包括当前最先进的深度学习方法）构成了重大挑战。为了解决这个问题，我们应用了一种基于深度学习的方法，用于在类别不平衡的情况下对$γ$ -氧化铝/Pt催化材料进行多类别语义分割。具体来说，我们使用加权焦点损失作为损失函数，并将其附加到U-Net的全卷积网络架构中。我们使用Dice相似性系数（DSC），召回率，精度和Hausdorff距离（HD）指标评估了我们结果的准确性，这些指标是关于地面实况和预测分割之间的重叠。我们采用的具有加权焦点损失函数的U-Net模型在$γ$ -氧化铝载体材料中实现了0.96 $\pm$ 0.003的平均DSC得分，并且在Pt NP分割任务中实现了0.84 $\pm$ 0.03的平均DSC得分。我们报告了$γ$ -氧化铝和Pt纳米颗粒分割在HD的第90百分位数处小于2 nm的平均边界重叠误差。通过对高角度环形暗场（HAADF）扫描透射电子显微镜（STEM）断层扫描重建的大型数据集进行深度学习辅助自动分割，在3D中可视化$γ$ -氧化铝的复杂表面形态及其与Pt NP的关系。
英文摘要：
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 $γ$-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 $γ$-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 $γ$-Alumina and Pt NPs segmentations. The complex surface morphology of the $γ$-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.
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