A wide range of techniques can be considered for segmentation of images of nanostructured surfaces. Manually segmenting these images is time-consuming and results in a user-dependent segmentation bias, while there is currently no consensus on the best automated segmentation methods for particular techniques, image classes, and samples. Any image segmentation approach must minimise the noise in the images to ensure accurate and meaningful statistical analysis can be carried out. Here we develop protocols for the segmentation of images of 2D assemblies of gold nanoparticles formed on silicon surfaces via deposition from an organic solvent. The evaporation of the solvent drives far-from-equilibrium self-organisation of the particles, producing a wide variety of nano- and micro-structured patterns. We show that a segmentation strategy using the U-Net convolutional neural network has some benefits over traditional automated approaches and has particular potential in the processing of images of nanostructured systems.

可以考虑使用多种技术对纳米结构表面的图像进行分割。手动分割这些图像非常耗时，并且会导致依赖于用户的分割偏差，而对于特定技术，图像类别和样本的最佳自动分割方法目前尚无共识。任何图像分割方法都必须将图像中的噪声降至最低，以确保可以进行准确而有意义的统计分析。在这里，我们开发了一种协议，用于分割通过从有机溶剂中沉积在硅表面形成的金纳米颗粒的2D组装图像的图像。溶剂的蒸发驱动了粒子的非平衡自组织，从而产生了各种各样的纳米结构和微观结构的图案。