Open Access

ARTICLE

Semi-Supervised Segmentation Framework for Quantitative Analysis of Material Microstructure Images

Yingli Liu^1,2, Weiyong Tang^1,2, Xiao Yang^1,2, Jiancheng Yin^3,*, Haihe Zhou^1,2

1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650093, China
2 Yunnan Key Laboratory of Computer Technologies Application, Kunming University of Science and Technology, Kunming, 650500, China
3 Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, 650093, China

* Corresponding Author: Jiancheng Yin. Email:

Computers, Materials & Continua 2026, 87(1), 20 https://doi.org/10.32604/cmc.2026.074681

Received 15 October 2025; Accepted 05 January 2026; Issue published 10 February 2026

Abstract

Quantitative analysis of aluminum-silicon (Al-Si) alloy microstructure is crucial for evaluating and controlling alloy performance. Conventional analysis methods rely on manual segmentation, which is inefficient and subjective, while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data. Furthermore, existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data. To address these issues, this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation. First, we introduce a Rotational Uncertainty Correction Strategy (RUCS). This strategy employs multi-angle rotational perturbations and Monte Carlo sampling to assess prediction consistency, generating a pixel-wise confidence weight map. By integrating this map into the loss function, the model dynamically focuses on high-confidence regions, thereby improving generalization ability while reducing manual annotation pressure. Second, we design a Boundary Enhancement Module (BEM) to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions. This module guides the model to focus on the boundary regions of adjacent particles, effectively resolving particle adhesion and improving segmentation accuracy. Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset (ASAD). Results indicate that the proposed method performs exceptionally well with scarce labeled data. Specifically, using only 5% labeled data, our method improves the Jaccard index and Adjusted Rand Index (ARI) by 2.84 and 1.57 percentage points, respectively, and reduces the Variation of Information (VI) by 8.65 compared to state-of-the-art semi-supervised models, approaching the performance levels of 10% labeled data. These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.

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Keywords

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