Automatic pore characterization in SEM images of foams using a fine-tuned segment anything model
Journal
Materials and Design
Journal Volume
262
Start Page
115529
ISSN
02641275
Date Issued
2026-02
Author(s)
Abstract
Identifying and analyzing pores in scanning electron microscopy (SEM) images of foams is a labor-intensive task, often requiring manual annotation that limits reproducibility and throughput. This study proposes an automated framework for pore characterization based on a fine-tuned Segment Anything Model (SAM). Low-rank adaptation (LoRA) and targeted data augmentation—including geometric transformations, color jittering, Gaussian blurring, and synthetic scratch generation—are employed to adapt SAM to the domain of foam microstructures. The resulting ViT-H2 model achieves high precision (0.90) and recall (0.90) across both monodisperse and polydisperse foams, while reducing errors in pore anisotropy ratio and angle to 1.93% and 5.83∘, respectively. The predicted pore masks closely align with expert annotations, capturing both pore size distributions and anisotropy characteristics with high fidelity. Notably, the method enables high-throughput pore analysis that integrates seamlessly into materials characterization workflows. These results demonstrate the effectiveness of fine-tuned foundation models for automated microstructure analysis, offering a scalable, data-driven approach for the design and discovery of advanced foams and other porous materials.
Subjects
Foam
Foundation model
Low-rank adaptation
Pore characterization
Publisher
Elsevier Ltd
Type
journal article