A reproducible U-net workflow for SEM/BSE bright-particle quantification.

Authors

Ibrahim M Ibrahim, Geoffrey Will, Stuart Bell

Published in

MethodsX. Volume 17. Pages 104134. Epub Sep 02, 2026.

Abstract

Thermal exposure of metallic alloys can promote the precipitation of deleterious secondary particles that degrade mechanical and corrosion performance, making their quantitative characterisation from SEM/BSE micrographs an important step in microstructural assessment. Quantifying precipitate regions in SEM/BSE micrographs of thermally aged steels can be difficult when image contrast, particle size, and particle connectivity change across conditions. This method describes a reproducible workflow for generating reference bright-particle masks, training a lightweight U-Net segmentation model, and converting predicted masks into conventional microstructural descriptors. The workflow combines contrast-enhanced preprocessing, condition-aware reference mask generation, image-level data splitting, overlapping patch extraction, supervised U-Net training, fixed-threshold inference, and connected-component analysis. It is intended for SEM/BSE datasets where bright regions are used as image-based indicators of Cr/Mo-rich deleterious particles, while recognising that crystallographic phase identification requires complementary evidence. In the present application, the reported performance metrics quantify agreement with threshold-derived reference masks on held-out micrographs; they should not be interpreted as validation against independent phase-resolved ground truth. The demonstrated applicability and reported performance are limited to 2205 duplex stainless steel aged at 850 °C and imaged at 5000× using the acquisition protocol described here. Transfer to other materials or imaging domains requires separate validation and may require image recalibration, model fine-tuning, transfer learning, or complete retraining.•The workflow provides a reproducible route from raw SEM/BSE images to binary masks and particle-level descriptors.•Image-level splitting and fixed inference settings reduce data leakage and post-hoc condition tuning.•The method preserves compatibility with conventional area fraction, particle count, particle size, and morphology measurements.

PMID:
42733781
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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