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NEXT MATERIALS 2025年 8卷

作者： Tung, Po-Yen Harrison, Richard J. Univ Cambridge Deparrment Earth Sci Cambridge England Univ Cambridge Deparrment Mat Sci & Met Cambridge England

The application of three-dimensional (3D) imaging techniques, such as X-ray tomography and focussed ion beam scanning electron microscopy (FIB-SEM), is increasingly widespread in microstructural analysis of natural materials. However, our ability to collect high-resolution tomographic datasets, each comprising thousands of twodimensional (2D) images with millions of pixels, far outstrips our ability to analyse them. Pixel-level segmentation of each 2D image is the first step in any analysis pipeline, but creates a considerable human bottleneck in the workflow that can now be overcome using machine learning. Although advanced pre-trained models such as the Segment Anything Model (SAM) have emerged, conventional segmentation workflows for 3D tomographic data remain limited in comparison. To tackle this, we propose a machine learning workflow that combines SAM with a few-shot learning framework, automating segmentation and minimising user bias. Using SAM, we generate precise annotations from a limited subset of 2D images through basic input prompts, such as points and boxes. These annotations serve as the training data for the few-shot learning model. We benchmark this workflow using a complex 3D FIB-SEM tomographic dataset of the C2 ungrouped carbonaceous chondrite WIS91600. With only 0.6 % of the training data, our method achieves an intersection over union (IoU) score of 80.62 % compared to the ground truth, significantly outperforming widely used methods that achieve a maximum IoU score of 67.07 %. The strong performance on the challenging meteorite dataset highlights its potential for broader application across materials and imaging modalities.

关键词： segmentation Few-shot learning Segment Anything Model (SAM) 3D tomographic datasets microstructure segmentation Machine learning in materials science High-resolution imaging analysis

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Superpixel-based principal feature clustering annotation method for dual-phase microstructure segmentation

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MATERIALS CHARACTERIZATION 2024年 218卷

作者： Lin, Shuanglan Xu, Lei Guo, Zhixing Zhang, Dingcheng Zeng, Pangwei Tang, Yuexin Pei, Hongliang Sichuan Univ Sch Mech Engn Chengdu 610065 Peoples R China PetroChina Southwest Oil & Gasfield Co Res Inst Nat Gas Technol Chengdu 610065 Peoples R China

Metallographic analysis is one of the most commonly used techniques by materials scientists for studying metal materials. The deep learning methods, which have been widely applied in metallographic images analysis, demonstrate excellent performance in this task. However, the optimization of deep learning models often relies on a substantial amount of accurately labeled samples for effective supervision. To address this issue, this paper proposes a novel automatic annotation method based on brightness and spatial distribution which is suitable for deep learning-based segmentation of optical dual-phase metallographic microstructure. The proposed automatic annotated method includes the superpixel segmentation, principal feature extraction, and clustering algorithm therefore it is referred as the superpixel-based principal feature clustering annotation (SPFCA) method. SPFCA employs discriminative criteria similar to those used by metallurgists to differentiate between metallographic structures. Furthermore, it can mitigate the occasional errors inherent in manual annotation, leading to improved performance compared to models trained with expert annotations. Experimental validation was conducted using four self-built datasets with different image qualities to test the performance of models from different perspective. Initially, hyperparameter optimization for the SPFCA method tailored to our dataset was performed. Subsequently, SPFCA was utilized to guide the optimization of the convolutional neural network employed for segmentation. The results demonstrate that the segmentation model optimized with SPFCA guidance achieved an F1 score of 0.9226 in the single dataset without the need for manual labeling, surpassing the segmentation models optimized with expert annotations.

关键词： Automatical annotation microstructure segmentation Superpixel segmentation Deep learning

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MATBOX: An Open-source microstructure Analysis Toolbox for microstructure generation, segmentation, characterization, visualization, correlation, and meshing

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SOFTWAREX 2022年 17卷

作者： Usseglio-Viretta, F. L. E. Patel, P. Bernhardt, E. Mistry, A. Mukherjee, P. P. Allen, J. Cooper, S. J. Laurencin, J. Smith, K. Natl Renewable Energy Lab Ctr Energy Convers & Storage Syst Golden CO 80401 USA Argonne Natl Lab Chem Sci & Engn Div Lemont IL 60439 USA Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Natl Renewable Energy Lab Computat Sci Ctr Golden CO 80401 USA Imperial Coll London Dyson Sch Design Engn London SW7 2DB England CEA LITEN 17 Rue Martyrs F-30854 Grenoble France

MATBOX is an easy-to-use, all-in-one, MATLAB application for microstructure numerical analysis, including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization, three-dimensional visualization, microstructure parameters correlation, and microstructure meshing. MATBOX was originally developed to analyse electrode microstructures for lithium-ion batteries;however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphic-User Interface. (C) 2021 Published by Elsevier B.V.

关键词： microstructure generation microstructure segmentation microstructure characterization microstructure meshing

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Automated microstructural segmentation and grain size measurement of Al plus SiC nanocomposites using advanced image processing techniques on backscattered electron images

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MATERIALS CHARACTERIZATION 2025年 222卷

作者： Harikrishna, Katika Nithin, Abeyram Davidson, M. J. Shri Vishnu Engn Coll Women Dept Mech Engn Bhimavaram 534202 India Sreenidhi Inst Sci & Technol Dept Mech Engn Hyderabad 501301 India Natl Inst Technol Dept Mech Engn Warangal 506004 India

Grain size analysis is crucial for understanding material properties, yet traditional manual methods are often time-consuming and labor-intensive. This study presents a novel approach utilizing Python's OpenCV, SciPy, and NumPy libraries for automated microstructure segmentation and grain size analysis of Al + SiC nanocomposites fabricated through powder metallurgy (PM). When segmenting backscattered electron (BSE) images, challenges such as noise, local contrast variations, inaccurate thresholding, fused grains, edge grain removal, and grain boundary separation arise. To address these, advanced image processing techniques were employed: Gaussian filtering reduced noise, and Contrast Limited Adaptive Histogram Equalization (CLAHE) enhanced local contrast, making grain boundaries more distinct. Automated thresholding was performed using Otsu's method to differentiate grains and boundaries, while morphological operations (erosion and dilation) refined the separation of fused grains. Edge grains were excluded using ***(), and the distance transform function clearly delineated grains and boundaries. Connected components analysis was used to identify and label distinct regions in the image, aiding in the determination of the number of grains. The algorithm was tested on multiple BSE images for robustness, with results compared to manual grain size measurements according to ASTM standards. A Bland-Altman plot and Pearson correlation were used to validate the algorithm, showing that the error is within the limits of agreement and the correlation coefficient of 0.98 demonstrates high accuracy in predicting grain sizes, maintaining a reasonable level of precision.

关键词： Automated grain size measurement Image processing Python programming language microstructure segmentation OpenCV Powder metallurgy

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Micro-KTNet: microstructure knowledge transfer learning for fiber masterbatch agglomeration recognition

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JOURNAL OF INDUSTRIAL TEXTILES 2025年 55卷

作者： Zhang, Xianqi Wei, Bing Hao, Kuangrong Gao, Lei Xie, Ruimin Wang, Huaping Donghua Univ Coll Informat Sci & Technol Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai Peoples R China Australias Commonwealth Sci & Ind Res Org CSIRO Waite Campus Urrbrae SA Australia Donghua Univ Coll Mat Sci & Engn State Key Lab Modificat Chem Fibers & Polymer Mat Shanghai Peoples R China

Fiber masterbatch production suffers from inherent agglomeration effect of high-concentration color masterbatches, negatively impacting color uniformity, particle dispersion, and thermal stability in fiber masterbatch, and ultimately the quality of fiber products. However, accurately recognizing and classifying the agglomeration in scanning electron microscopy (SEM) images remain a challenge due to the complexity of agglomeration status, difficulty in distinguishing micro-size agglomeration, and limited data availability. To address this challenge, this paper proposes a novel microstructure recognition architecture, named Micro-KTNet, which is designed for segmenting SEM images of fiber masterbatch aggregation. First, Micro-KTNet leverages transferable microstructural features to initialize the proposed network, mitigating the impact of limited data. Then, to handle multi-scale features in the microstructure, an encoding-decoding structure is constructed and skip connections are used to transfer the output features of the encoding layer. Finally, the decoder incorporates a novel attention module to effectively process both global and local features. To evaluate Micro-KTNet, a unique fiber masterbatch SEM database is built as the benchmark for micro-size agglomeration recognition. Experimental results indicate that Micro-KTNet surpasses existing state-of-the-art methods and improves the recognition precision from 75. 37% to 87. 04%.

关键词： Fiber masterbatch agglomeration effects encoder-decoder framework transfer learning microstructure segmentation attention module

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SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation

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COMPUTATIONAL MATERIALS SCIENCE 2025年 246卷

作者： Abebe, Waqwoya Strube, Jan Guo, Luanzheng Tallent, Nathan R. Bel, Oceane Spurgeon, Steven Doty, Christina Jannesari, Ali Pacific Northwest Natl Lab Adv Computat & Maths Div Richland WA 99354 USA Iowa State Univ Ames IA 50010 USA Pacific Northwest Natl Lab Phys Detect Syst & Deployment Richland WA 99354 USA Univ Oregon Inst Fundamental Sci Eugene OR 97403 USA

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries located? what segments are logically similar? - change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming;domain images (micrographs) can be exponentially diverse;and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: : given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. We apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero- shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).

关键词： microstructure segmentation Segment anything model Semantic booster

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Investigating local orientation methods to segment microstructure with 3D solid texture

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IET IMAGE PROCESSING 2018年第7期12卷 1265-1272页

作者： Al Darwich, Ranya Babout, Laurent Lodz Univ Technol Inst Appl Comp Sci Lodz Poland

This study investigates local orientation-based approaches to the complex problem of pattern segmentation in three-dimensional (3D) texture image. The current problem focuses on the extraction of so-called lamellar colonies in titanium alloy, which, from the materials science and engineering point of view, are microstructural features that play a fundamental role on crack propagation and bifurcation during mechanical loading. Methods based on local orientation estimation extend the notion of using local gradient to reveal variation of semi-planar pattern orientation in the 3D image. The study introduces a computational approach that accelerates the calculation of the eigenvectors from the local matrices of inertia of all voxels composing the 3D image. Then different paths are proposed to segment colonies or inter-colony boundaries, i.e. polar orientation map and minimum scalar product map, in order to delimitate regions of similar orientations. The investigated segmentation methods have been compared with other methods that are mainly based on the popular solution of filter banks. Tests, which have been performed on both synthetic and real 3D images, show that the proposed local orientation-based methods better delineate object boundaries than the counterparts.

关键词： crystal microstructure image segmentation image texture computer graphics titanium alloys materials science computing gradient methods eigenvalues and eigenfunctions matrix algebra matrices materials science inter-colony boundaries eigenvectors semiplanar pattern orientation local gradient local orientation estimation mechanical loading bifurcation crack propagation titanium alloy lamellar colonies extraction three-dimensional texture image pattern segmentation 3D solid texture microstructure segmentation local orientation methods

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Deep learning in alloy material microstructures: Application and prospects

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MATERIALS TODAY COMMUNICATIONS 2023年 37卷

作者： Che, Lun He, Zhongping Zheng, Kaiyuan Si, Tianyu Ge, Meiling Cheng, Hong Zeng, Lingrong Chengdu Univ Coll Mech Engn Chengdu Peoples R China

This review explores the applications, challenges, and prospects of deep learning in the microstructure analysis of alloy materials. First, it introduces the significance of alloy materials in modern industry, along with the continuous advancements in microscopy techniques and deep learning. Next, it briefly outlines the fundamental concepts and workflow of deep learning. In the critical section of this review, it elucidates how deep learning, through learning and training, can extract features from a large volume of alloy microstructure data and accurately classify these microstructures. Furthermore, deep learning can also be applied to tasks such as image segmentation in alloy microstructure images, including object detection and instance segmentation, facilitating the extraction of pertinent information from complex images. This, in turn, allows the establishment of the "microstructure-performance" relationship for effective performance prediction. Finally, it summarizes the prospects of deep learning in alloy material applications and the challenges related to data acquisition, model training,and performance optimization.

关键词： Alloy materials Deep learning microstructure classification microstructure segmentation microstructure detection Performance prediction

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Unsupervised segmentation of microstructural images of steel using data mining methods

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COMPUTATIONAL MATERIALS SCIENCE 2022年第0期201卷 110855-110855页

作者： Kim, Hoheok Arisato, Yuuki Inoue, Junya Univ Tokyo Inst Ind Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778574 Japan Univ Tokyo Grad Sch Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

A novel and efficient data mining method for the segmentation of microstructural images of low-carbon steel is presented. Microstructural characterization has been the focus of many works in the field of materials science because microstructure is the fundamental element in understanding the link between process and property. Recently, deep-learning-based methods have been actively employed for microstructural classification since it has shown outstanding performance for solving image classification problems. However, previous applications of deep learning models to microstructural classification revealed limitations in that not only do they require the time-consuming labeling process but it is also still difficult to obtain a satisfactory result, especially for steel microstructures containing substances developed by displacive transformation. In this study, we propose a rulebased segmentation method that not only works without labeled images but also requires no prior knowledge of the number of microstructural constituents in each image. This unsupervised inference algorithm captures the morphological features of each microstructure and automatically finds the optimal number of microstructures having similar characteristics using a Bayesian Gaussian mixture model. The viability of our method is demonstrated by qualitative and quantitative evaluations with optical microscopy images of steel composed of different microstructures taken under different imaging conditions.

关键词： Steel microstructure microstructure segmentation Machine learning And unsupervised learning

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