Electron tomography (ET) can provide comprehensive and detailed structural information on the polyamide selective layer of reverse osmosis membranes. Albeit powerful, the segmentation of low-contrast grayscale images from transmission electron microscope tomography remains a challenge. In this study, a neural network-assisted image segmentation method was developed to solve this problem. A well-trained neural network based on the U-Net model was able to automatically identify the boundary between polymer and vacuum, which reached a 93.0% compatibility with the baseline within a 0.5 h effort of data segmentation, significantly improved upon other segmentation methods. The results were compared with those of a previous study and conventional characterization results for validation. In addition, we performed topography analyses to quantitatively characterize the surface geometry and proposed an entire “sample preparation to 3D structural model” workflow that calibrates the data sets and minimizes tedious manual inputs of ET. For the first time, ET is qualified to be a powerful quantitative characterization technique for separation membranes and other polymer materials widely used in energy and environmental applications.