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Semantic and instance segmentation deep learning methods for nanoparticles detection
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AI
DOI：10.1016/j.mtcomm.2025.112074.png)
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Abstract
En 中文
Nanoparticle segmentation is a critical task in nanotechnology, enabling precise characterization of physical properties such as size, shape, and perimeter. This study compares four segmentation methods: U-Net (semantic segmentation), Mask R-CNN (instance segmentation), k-means clustering, and adaptive thresholding, to identify the most accurate and reliable approach. With a dataset of only 102 images, deep learning-based methods were hypothesized to outperform traditional techniques. Performance was evaluated using metrics such as F1 score and segmentation accuracy. U-Net emerged as the top performer across all image categories, achieving an overall accuracy of 96.65% and an F1 score of 0.909. For easy-to-segment images, U-Net achieved an accuracy of 98.95% and an F1 score of 0.951; for medium difficulty, 95.27% accuracy and 0.906 F1 score; and for hard-to-segment images, 92.76% accuracy and an F1 score of 0.87. These results demonstrate the superior accuracy of U-Net over Mask R-CNN, k-means clustering, and adaptive thresholding, highlighting the potential of CNN-based models to significantly enhance nanoparticle characterization and providing a robust foundation for future research.
Keywords:
Nanoparticles
Segmentation
Deep learning
Semantic
Instance
Journal
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4.5
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1.5W
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3.7W