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A reproducible U-net workflow for SEM/BSE bright-particle quantification
Journal article   Open access   Peer reviewed

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

Ibrahim M. Ibrahim, Geoffrey Will and Stuart Bell
MethodsX, Vol.17, pp.1-14
2026
DOI: https://doi.org/10.1016/j.mex.2026.104134
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A reproducible U-net workflow for SEM/BSE bright-particle quantification1.41 MBDownloadView
Published Version Open Access CC BY V4.0

Abstract

Bright-particle quantification CNN Connected-component analysis Deep learning Duplex stainless steel Microstructure segmentation Reference mask generation SEM/BSE image analysis Semantic segmentation U-net segmentation
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.

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