Three‐dimensional reconstruction of porous polymer films from FIB‐SEM nanotomography data using random forests

Dataset and code used in M. Röding, et al, "Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests", published in Journal of Microscopy, 2020. In this work, we develop a segmentation method for focused ion beam scanning electron microscopy (FIB-SEM) data acquired by volumetric imaging of porous polymer films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. This type of polymer films are used for controlled release applications. Based on manual segmentation of a fraction of the data, a random forest classifier is trained and applied to the full data set. Here, raw data, manual segmentations, and the Matlab code used for all steps in the analysis are supplied.

本数据集及配套代码源自M. Röding等人于2020年发表在《显微学杂志（Journal of Microscopy）》上的论文《利用随机森林从FIB-SEM纳米断层扫描数据重建多孔聚合物薄膜》。本研究开发了一种针对聚焦离子束扫描电子显微镜（focused ion beam scanning electron microscopy, FIB-SEM）数据的分割方法，该类数据源自乙基纤维素（ethyl cellulose, EC）与羟丙基纤维素（hydroxypropyl cellulose, HPC）聚合物共混物制备的多孔聚合物薄膜的体积成像。此类聚合物薄膜可应用于控释领域。基于部分数据的手动分割结果，本研究训练了随机森林分类器并将其应用于完整数据集。本数据集包含原始数据、手动分割结果以及用于完成全部分析流程的Matlab代码。