SAMM: a general-purpose segmentation model for material micrographs based on the segment anything model 2
Year: 2026
Type: article
Abstract: Precise segmentation is fundamental to quantitative microstructural analysis, a cornerstone of establishing processing-structure-property relationships in materials science. However, the generalization of deep learning models remains a key challenge, with performance often degrading significantly when applied to unseen material systems or imaging conditions. This limitation underscores the urgent need for a universal segmentation model. Herein, we propose SAMM, a general-purpose segmentation model for material micrographs, developed by fine-tuning the Segment Anything Model 2 (SAM2) on a large-scale, diverse dataset. Our approach integrates a full-parameter fine-tuning strategy, a cross-scale feature fusion module for enhanced detail resolution, and a hybrid loss function to ensure both pixel-level accuracy and structural integrity. Consequently, SAMM demonstrates superior generalization across 13 diverse material datasets. For instance, it achieves a mean Intersection-over-Union (mIoU) of 76.63% on challenging nickel-based superalloy micrographs, outperforming the next-best baseline by 10.3%. In segmenting additive manufacturing powders, SAMM delivers near-perfect results (98.13% mIoU). Crucially, SAMM exhibits exceptional zero-shot generalization, achieving up to 97.41% mIoU on datasets entirely unseen during training. This work not only presents a robust framework for universal microstructure segmentation but also provides a comprehensive, publicly available dataset to foster further research in this domain.
Source: Advanced Powder Materials
Authors: Jiahao Tu, Zi Wang, Weifu Li, Liming Tan, Lan Huang, Feng Liu
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $840
Awards: G3389680642, G34197466, G8362924633
OpenAlex ID: W7127335250