标题： 多任务DeltaNet：基于变化检测的原位ETEM图像分割及其在碳气化动力学中的应用 显示英文标题

标题： MultiTaskDeltaNet: Change Detection-based Image Segmentation for Operando ETEM with Application to Carbon Gasification Kinetics

摘要： 将原位透射电子显微镜（TEM）成像转化为用于固态反应的空间分辨操作表征工具，需要对动态演化的特征进行自动化、高精度的语义分割。 然而，传统的语义分割深度学习方法由于标注数据稀缺、感兴趣视觉模糊的特征以及小物体场景而经常遇到限制。 为解决这些挑战，我们引入了MultiTaskDeltaNet（MTDN），一种创新的深度学习架构，创造性地将分割任务重新概念化为变化检测问题。 通过实现一个带有U-Net主干的唯一孪生网络，并使用配对图像来捕捉特征变化，MTDN有效地利用最少的数据生成高质量的分割结果。 此外，MTDN利用多任务学习策略来利用感兴趣的物理特征之间的相关性。 在使用丝状碳气化原位环境TEM（ETEM）视频数据进行评估时，MTDN在准确描绘精细结构特征方面明显优于传统分割模型。 值得注意的是，MTDN在预测小而视觉模糊的物理特征方面比传统分割模型提高了10.22%的性能。 这项工作弥合了深度学习与实际TEM图像分析之间的几个关键差距，推动了复杂实验环境中纳米材料的自动化表征。 显示英文摘要

摘要： Transforming in-situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often encounter limitations due to the scarcity of labeled data, visually ambiguous features of interest, and small-object scenarios. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively utilizes minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to leverage correlations between physical features of interest. In an evaluation using data from in-situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges several key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.