ABSTRACT Volume electron microscopy (vEM) is a powerful technique that enables 3D visualization of biological structures at the nanometer scale. However, vEM imaging relies on sequential scanning of 2D images, and due to section thickness limitations, the axial resolution is significantly lower than the lateral resolution. In this paper, we propose the vEMINR, an ultra‐fast isotropic reconstruction method based on implicit neural representation (INR). This method enhances the reconstruction quality of vEM images by learning the true degradation patterns of low‐resolution images, and significantly accelerates the reconstruction process by utilizing the efficient parameterization and a continuous function representation of INR. In experiments on 11 public datasets, vEMINR outperforms mainstream methods with over tenfold faster reconstruction and higher accuracy. vEMINR substantially improved the accuracy of organelle and neuron reconstruction from vEM. Overall, the excellent reconstruction time efficiency of vEMINR enables high‐throughput processing of terabyte‐scale vEM datasets while maintaining reconstruction accuracy. We believe that it will play a significant role in large‐scale vEM image reconstruction and related research fields.
Yang et al. (Fri,) studied this question.