用实验室数据训练模型，自动分割同步辐射动态成像数据。
Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography
- 将高质量离线数据转换为在线数据的训练样本。
- 分割准确率达94.7%交并比，媲美人工标注可靠性。
- 适合需快速分析大量动态三维成像数据的研究者。
原位同步辐射X射线断层扫描可实现材料动态过程研究，但因成像伪影（如环状、杯状效应）及训练数据有限，自动化分割仍具挑战。本文提出一种基于深度学习的分割方法，通过将高质量离线实验室数据进行修正，用于训练模型以分割原位同步辐射数据，以金属氧化物溶解研究为例。采用改进的SegFormer架构，模型分割性能达94.7% IoU，与人工标注间一致性（94.6% IoU）相当，表明已达到该任务的实际上限。相比人工分割，单个3D数据集处理时间缩短两个数量级。该方法在实验过程中显著形态变化下仍保持鲁棒性，且仅基于静态样本训练即可实现。该策略可广泛应用于多种材料体系，高效解析典型原位实验中产生的海量时序断层数据。
原文摘要 · Abstract (English)
In situ synchrotron X-ray computed tomography enables dynamic material studies. However, automated segmentation remains challenging due to complex imaging artefacts - like ring and cupping effects - and limited training data. We present a methodology for deep learning-based segmentation by transforming high-quality ex situ laboratory data to train models for segmentation of in situ synchrotron data, demonstrated through a metal oxide dissolution study. Using a modified SegFormer architecture, our approach achieves segmentation performance (94.7% IoU) that matches human inter-annotator reliability (94.6% IoU). This indicates the model has reached the practical upper bound for this task, while reducing processing time by 2 orders of magnitude per 3D dataset compared to manual segmentation. The method maintains robust performance over significant morphological changes during experiments, despite training only on static specimens. This methodology can be readily applied to diverse materials systems, enabling the efficient analysis of the large volumes of time-resolved tomographic data generated in typical in situ experiments across scientific disciplines.
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