Quantification and Classification of Carbon Nanotubes in Electron Micrographs using Vision Foundation Models

• URL: http://arxiv.org/abs/2601.06673v1
• Date: Sat, 10 Jan 2026 20:22:58 GMT
• Title: Quantification and Classification of Carbon Nanotubes in Electron Micrographs using Vision Foundation Models
• Authors: Sanjay Pradeep, Chen Wang, Matthew M. Dahm, Jeff D. Eldredge, Candace S. J. Tsai,
• Abstract summary: This work presents a unified framework leveraging vision foundation models to automate the quantification and classification of CNTs in electron microscopy images.<n>We introduce an interactive quantification tool built on the Segment Anything Model (SAM) that segments particles with near-perfect accuracy using minimal user input.<n>Second, we propose a novel classification pipeline that utilizes these segmentation masks to spatially constrain a DINOv2 vision transformer, extracting features exclusively from particle regions while suppressing background noise.<n> Evaluated on a dataset of 1,800 TEM images, this architecture achieves 95.5% accuracy in distinguishing between four different CNT morphologies, significantly outperforming the current baseline despite using a
• Score: 1.8969168959157112
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate characterization of carbon nanotube morphologies in electron microscopy images is vital for exposure assessment and toxicological studies, yet current workflows rely on slow, subjective manual segmentation. This work presents a unified framework leveraging vision foundation models to automate the quantification and classification of CNTs in electron microscopy images. First, we introduce an interactive quantification tool built on the Segment Anything Model (SAM) that segments particles with near-perfect accuracy using minimal user input. Second, we propose a novel classification pipeline that utilizes these segmentation masks to spatially constrain a DINOv2 vision transformer, extracting features exclusively from particle regions while suppressing background noise. Evaluated on a dataset of 1,800 TEM images, this architecture achieves 95.5% accuracy in distinguishing between four different CNT morphologies, significantly outperforming the current baseline despite using a fraction of the training data. Crucially, this instance-level processing allows the framework to resolve mixed samples, correctly classifying distinct particle types co-existing within a single field of view. These results demonstrate that integrating zero-shot segmentation with self-supervised feature learning enables high-throughput, reproducible nanomaterial analysis, transforming a labor-intensive bottleneck into a scalable, data-driven process.

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