Segmentation of Nano-Particles from SEM Images Using Transfer Learning and Modified U-Net

Abstract

Nanomaterials, owing to their distinctive features, are crucial across numerous scientific domains, especially in materials science and nanotechnology. Precise segmentation of Scanning Electron Microscope (SEM) images is essential for evaluating attributes such as nanoparticle dimensions, morphology, and distribution. Conventional image segmentation techniques frequently prove insufficient for managing the intricate textures of SEM images, resulting in a laborious and imprecise process. In this research, a modified U-Net architecture is presented to tackle this challenge, utilizing a ResNet50 backbone pre-trained on ImageNet. This model utilizes the robust feature extraction abilities of ResNet50 alongside the effective segmentation performance of U-Net, hence improving both accuracy and computational efficiency in TiO2 nanoparticle segmentation. The suggested model was assessed using performance metrics including accuracy, precision, recall, IoU, and Dice Coefficient. The results indicated a high segmentation accuracy, demonstrated by a Dice score of 0.946 and an IoU of 0.897, with little variability reflected in standard deviations of 0.002071 and 0.003696, respectively, over 200 epochs. The comparison with existing methods demonstrates that the proposed model surpasses previous approaches by attaining enhanced segmentation accuracy. The modified U-Net design serves as an excellent technique for accurate nanoparticle segmentation in SEM images, providing substantial enhancements compared to traditional approaches. This progress indicates the model's potential for wider applications in nanomaterial research and characterization, where precise and efficient segmentation is essential for analysis.