Quantification and Classification of Carbon Nanotubes in Electron Micrographs using Vision Foundation Models
🤖 AI Summary
Manual segmentation of carbon nanotubes in transmission electron microscopy (TEM) images is time-consuming and subjective, hindering high-throughput and reproducible morphological analysis. This work proposes the first unified framework that integrates the zero-shot segmentation model SAM with the self-supervised vision transformer DINOv2, enabling instance-level automatic quantification and precise classification of carbon nanotubes into four morphological categories through interactive segmentation and spatially constrained feature extraction. Requiring only minimal annotated data, the method achieves a classification accuracy of 95.5% on a dataset of 1,800 TEM images, significantly outperforming existing baselines. This approach establishes an efficient and scalable paradigm for high-throughput characterization of nanomaterials.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
carbon nanotubes
electron microscopy
quantification
classification
morphology
Innovation
Methods, ideas, or system contributions that make the work stand out.
vision foundation models
Segment Anything Model (SAM)
DINOv2
carbon nanotube classification
instance-level segmentation
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