Automatic pore characterization in SEM images of foams using a fine-tuned segment anything model

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. • Fine-tuned Segment Anything Model enables automated pore annotation in SEM foams. • Low-rank adaptation and scratch augmentation enhance recognition of small pores. • Accurately distinguishes tightly packed pores and avoids false merging in dense foams. • Real-time masks support high-throughput pore size and anisotropy characterization. • Anisotropy ratio error ∼ 2% and angle error ∼ 6 ∘ , confirming reliability.