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1. Machine learning for materials microstructures: A survey of applications and methodologies

   https://doi.org/10.1016/j.actamat.2026.122041  

   Barmak, K; Rickman, JM (April 2026, Acta Materialia)

   We present here a survey of important machine learning (ML) methodologies and associated applications that have led to recent major advances in the acquisition and interpretation of materials microstructures. These advances have enabled, for example, the automated processing of micrographs to identify defects, such as grain and interphase boundaries, and the subsequent classification of structures and compilation of grain statistics for use in quantitative microstructural characterization. To summarize the role of ML in this domain, we first outline different approaches to image segmentation and feature extraction and then describe various microscopies and associated distinctive issues relating to image partitioning and image object identification. For these microscopies, we review illustrative studies in which ML has played a crucial role in parsing the resulting micrographs. Next, we highlight the contributions of ML in microstructural interrogation and modeling by considering various strategies for image interpretation that exploit, for example, neural networks (NN) in tasks such as classification and clustering. As NNs have also furthered the goals of microstructural optimization and inverse design, we also examine how these tools have been employed to create materials having desirable properties. Finally, we illustrate the use of correlative statistical methods to assess and predict the occurrence of microstructural anomalies (e.g., abnormal grain growth) and report on progress in the creation and curation of microstructural repositories that facilitate data sharing. The aim of this Overview is to make the case that ML tools have become, in a relatively short time, indispensible aids for microstructural image processing and interpretation. 

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2. Prevalence and practices of immunofluorescent cell image processing: a systematic review

   https://doi.org/10.3389/fncel.2023.1188858  

   Helmbrecht, Hawley; Lin, Teng-Jui; Janakiraman, Sanjana; Decker, Kaleb; Nance, Elizabeth (July 2023, Frontiers in Cellular Neuroscience)

   BackgroundWe performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field. MethodsWe completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software. ResultsOf the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research. DiscussionLess than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods. ConclusionImage processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor. 

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3. Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges

   https://doi.org/10.1186/s13075-021-02716-3  

   Konnaris, Maxwell A.; Brendel, Matthew; Fontana, Mark Alan; Otero, Miguel; Ivashkiv, Lionel B.; Wang, Fei; Bell, Richard D. (December 2022, Arthritis Research & Therapy)

   Abstract Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems. 

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4. EBSD‐Based Calibration of Differential Stress From Experimentally Deformed Black Hills Quartzite Using the Perimeter‐Area Fractal Dimension

   https://doi.org/10.1029/2024JB030866  

   Johnson, Scott E; Song, Won Joon; Anderson, Erik K; Gerbi, Christopher C; Vel, Senthil S; Prior, David J; Stipp, Michael (October 2025, Journal of Geophysical Research: Solid Earth)

   Abstract Microstructures that preserve quantitative information about deformation conditions during viscous flow of crystalline materials are rare, the most common example being the dynamically recrystallized grain or subgrain size to infer differential stress. There are many instances in which identification of recrystallized grains or subgrains is challenging. Thus, another microstructural attribute that relates to differential stress would be useful. We use electron backscatter diffraction (EBSD) data from experimentally deformed Black Hills quartzite to show that the perimeter‐area fractal dimension of quartz aggregates (the slope of the log‐log relationship between perimeter and diameter), which we term the grain boundary dimension (GBD), strongly correlates with differential stress (σ) in rocks deformed by dislocation creep. Unlike traditional methods for estimating differential stress, theGBDmethod does not require identification of recrystallized grains or subgrains. Analysis of 9 samples yields both power‐law and logarithmic calibrations (σin MPa) and , which show excellent agreement with published grain‐size piezometers. Analysis of kernel average misorientation maps, guided by theoretical considerations, suggests thatGBDdevelops through heterogeneous grain boundary migration related to spatial variation of driving force from dislocations and dislocation walls in adjacent grains. Our calibrations cover dislocation‐creep conditions in which local grain boundary migration and subgrain rotation are the main dynamic recrystallization processes. Further work is needed to refine the calibration, test extrapolation to natural conditions, assess its applicability to general‐shear deformation, and extend the method to other minerals such as calcite, rock salt, olivine and ice. 

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5. Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials

   https://doi.org/10.1038/s41524-025-01701-7  

   Nandishwara, Kshithij Mysore; Cheng, Shuan; Liu, Pengjun; Zhu, Huimin; Guo, Xiaoyu; Massabuau, Fabien C-P; Hoye, Robert_L Z; Sun, Shijing (December 2025, npj Computational Materials)

   Abstract Microstructural design is crucial yet challenging for thin-film semiconductors, creating barriers for new materials to achieve practical applications in photovoltaics and optoelectronics. We present the Daisy Visual Intelligence Framework (Daisy), which combines multiple AI models to learn from historical microscopic images and propose new synthesis conditions towards desirable microstructures. Daisy consists of an image interpreter to extract grain and defect statistics, and a reinforcement-learning-driven synthesis planner to optimize thin-film morphology. Using Ag-Bi-I perovskite-inspired materials as a case study, Daisy achieved over 120× and 87× acceleration in image analysis and synthesis planning, respectively, compared to manual methods. Processing parameters for AgBiI₄were optimized from over 1700 possible synthesis conditions within 3.5 min, yielding experimentally validated films with no visible pinholes and average grain sizes 14.5% larger than the historical mean. Our work advances computational frameworks for self-driving labs and shedding light on AI-accelerated microstructure development for emerging thin-film materials. 

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