From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data
Abstract
Abstract X-ray tomography enables the nondestructive characterization of materials, and recent advances in micro-CT imaging technology have accelerated the acquisition and reconstruction of volumetric data. However, rapid scientific interpretation remains limited by image segmentation, which typically requires manual thresholding, user prompting, or material-specific model training before meaningful analysis can begin. We present a zero-setup deployment framework for multi-phase segmentation of synchrotron X-ray tomography data that produces interpretable segmentations of previously unseen tomography datasets without user prompting or additional retraining during deployment. The framework combines a material-agnostic mask preparation strategy based on common structural concepts in tomography data such as background, sample, bright regions, dark-gray regions, light-gray regions, and porosity, with a pretrained semantic segmentation network to generate diagnostic-quality segmentations within minutes of reconstruction. Unlike conventional deep learning pipelines that require dataset-specific annotations and retraining, the proposed approach can be applied immediately to new scans, enabling rapid assessment of experimental quality and preliminary microstructural analysis during ongoing beamline experiments. Users can subsequently refine or fine-tune the generated masks when application-specific segmentation is required. Experiments on held-out and qualitatively evaluated…
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