Improving the segmentation of scanning probe microscope images using convolutional neural networks
Improving the segmentation of scanning probe microscope images using convolutional neural networks
复制标题
利用卷积神经网络改进扫描探针显微镜图像的分割
DOI：
10.1088/2632-2153/abc81c
复制
发表时间：
2021-03-01
影响因子：
6.8
通讯作者：
Hunsicker, Eugenie
中科院分区：
文献类型：
--
作者：
Farley, Steff;Hodgkinson, Jo E. A.;Hunsicker, Eugenie
可以考虑广泛的技术来分割纳米结构表面的图像。手动分割这些图像是耗时的，并且导致依赖于用户的分割偏差，而目前对于特定技术、图像类别和样本的最佳自动分割方法没有共识。任何图像分割方法都必须将图像中的噪声降至最低，以确保能够进行准确和有意义的统计分析。在这里，我们开发了用于分割通过有机溶剂沉积在硅表面形成的金纳米颗粒的2D组件的图像的协议。溶剂的蒸发驱动粒子远离平衡的自组织，产生各种各样的纳米和微结构图案。研究表明，基于U-Net卷积神经网络的图像分割方法比传统的自动分割方法有一定的优势，在纳米结构系统的图像处理中具有独特的潜力。
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.