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This paper introduces an automated pipeline for grain size estimation in microscopy images by adapting Cellpose-SAM with topology-aware gradient tracking and integrating it with an ASTM E112 Jeffries planimetric module. The pipeline addresses the challenge of extracting standardized metallurgical metrics from microscopy images, which is difficult due to complex grain morphology and the data demands of supervised segmentation. Results show the adapted pipeline maintains topological separation and achieves a mean absolute percentage error (MAPE) as low as 1.50% in predicting the ASTM grain size number (G) with only two training samples.
You can now estimate grain size in microscopy images with surprising accuracy (1.5% MAPE) using just two training samples, thanks to a clever adaptation of Cellpose-SAM.
Extracting standardized metallurgical metrics from microscopy images remains challenging due to complex grain morphology and the data demands of supervised segmentation. To bridge foundational computer vision with practical metallurgical evaluation, we propose an automated pipeline for dense instance segmentation and grain size estimation that adapts Cellpose-SAM to microstructures and integrates its topology-aware gradient tracking with an ASTM E112 Jeffries planimetric module. We systematically benchmark this pipeline against a classical convolutional network (U-Net), an adaptive-prompting vision foundation model (MatSAM) and a contemporary vision-language model (Qwen2.5-VL-7B). Our evaluations reveal that while the out-of-the-box vision-language model struggles with the localized spatial reasoning required for dense microscopic counting and MatSAM suffers from over-segmentation despite its domain-specific prompt generation, our adapted pipeline successfully maintains topological separation. Furthermore, experiments across progressively reduced training splits demonstrate exceptional few-shot scalability; utilizing only two training samples, the proposed system predicts the ASTM grain size number (G) with a mean absolute percentage error (MAPE) as low as 1.50%, while robustness testing across varying target grain counts empirically validates the ASTM 50-grain sampling minimum. These results highlight the efficacy of application-level foundation model integration for highly accurate, automated materials characterization. Our project repository is available at https://github.com/mueez-overflow/ASTM-Grain-Size-Estimator.
