Microscopy images are usually analyzed qualitatively or manually and there is a need for autonomous quantitative analysis of objects. In this paper, we present a physics-based computational model for accurate segmentation and geometrical analysis of one-dimensional irregular and deformable objects from microscopy images. This model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. The model is tested on Ag nanowires, and successfully segments and analyzes their geometrical characteristics including length, width and distributions. The function of the algorithm is not undermined by the size, number, density, orientation and overlapping of objects in images. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99% accuracy, while current machine learning and computational models suffer from inaccuracy and inability to segment overlapping objects. Nano1D can analyze one-dimensional (1D) nanoparticles including nanowires, nanotubes, nanorods in addition to other 1D features of microstructures like microcracks, dislocations etc.

翻译：显微图像通常通过定性或手动方式进行分析，因此亟需对物体实现自主定量分析。本文提出一种基于物理学的计算模型，用于从显微图像中精确分割和几何分析一维不规则及可变形物体。该模型命名为Nano1D，包含预处理、分割、重叠物体分离和几何测量四个步骤。模型在银纳米线上进行了测试，成功分割并分析了其长度、宽度和分布等几何特征。该算法功能不受图像中物体尺寸、数量、密度、取向和重叠程度的影响。模型的主要优势在于能以超过99%的准确率成功分割并分析重叠物体，而现有机器学习及计算模型存在不准确性且无法分割重叠物体的问题。Nano1D可分析一维（1D）纳米粒子（包括纳米线、纳米管、纳米棒）以及微裂纹、位错等其他一维微观结构特征。