从重建到解释：X射线断层扫描数据的零设置多相分割
From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data
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中文总结 AI 辅助
研究针对X射线断层扫描数据快速解释受限问题，提出零设置多相分割框架，结合材料无关掩码准备策略与预训练语义分割网络，能快速生成诊断级分割，支持近实时束线反馈与可扩展成像工作流程。
中文摘要 AI 辅助
X射线断层扫描能够对材料微观结构进行无损表征，微CT成像的进展加速了体积数据采集和重建。然而，快速解释仍受图像分割限制，通常需要手动阈值处理、用户提示或特定材料模型训练。我们提出了一个用于同步加速器X射线断层扫描数据多相分割的零设置框架，无需用户输入或重新训练就能为未见数据集生成可解释的掩码。该框架将与材料无关的掩码准备策略和预训练的语义分割网络相结合，可直接应用于新扫描并在重建后几分钟内产生诊断级分割，支持近实时束线反馈和可扩展的人工智能辅助科学成像工作流程。
英文摘要
X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction. However, rapid interpretation remains limited by image segmentation, which often requires manual thresholding, user prompting, or material-specific model training. We present a zero-setup framework for multi-phase segmentation of synchrotron X-ray tomography data that generates interpretable masks for previously unseen datasets without user input or retraining during deployment. The framework combines a material-agnostic mask preparation strategy with a pretrained semantic segmentation network. It represents commonly occurring structural regions as background, sample, bright, dark-gray, light-gray, and porosity masks. Unlike conventional deep learning pipelines that require dataset-specific annotations and retraining, the proposed framework can be applied directly to new scans and produce diagnostic-level segmentations within minutes of reconstruction. This enables rapid assessment of scan quality, sample morphology, porosity, and attenuation variations during ongoing beamline experiments. The generated masks can later be manually refined or used to fine-tune application-specific models when greater accuracy or material-specific labeling is required. Evaluation on held-out synchrotron micro-CT images and qualitative testing on additional datasets demonstrate consistent and physically meaningful segmentations across varying samples and imaging conditions. The framework also substantially outperforms conventional intensity-based thresholding. By connecting high-speed reconstruction with immediate interpretation, the approach supports near-real-time beamline feedback and scalable AI-assisted scientific imaging workflows.
