Self-Supervised Super-Resolution Approach for Isotropic Reconstruction
  of 3D Electron Microscopy Images from Anisotropic Acquisition
Mohammad Khateri Morteza Ghahremani Alejandra Sierra Jussi Tohka
Three-dimensional electron microscopy (3DEM) is an essential technique to
investigate volumetric tissue ultra-structure. Due to technical limitations and
high imaging costs, samples are often imaged anisotropically, where resolution
in the axial direction ($z$) is lower than in the lateral directions $(x,y)$.
This anisotropy 3DEM can hamper subsequent analysis and visualization tasks. To
overcome this limitation, we propose a novel deep-learning (DL)-based
self-supervised super-resolution approach that computationally reconstructs
isotropic 3DEM from the anisotropic acquisition. The proposed DL-based
framework is built upon the U-shape architecture incorporating
vision-transformer (ViT) blocks, enabling high-capability learning of local and
global multi-scale image dependencies. To train the tailored network, we employ
a self-supervised approach. Specifically, we generate pairs of anisotropic and
isotropic training datasets from the given anisotropic 3DEM data. By feeding
the given anisotropic 3DEM dataset in the trained network through our proposed
framework, the isotropic 3DEM is obtained. Importantly, this isotropic
reconstruction approach relies solely on the given anisotropic 3DEM dataset and
does not require pairs of co-registered anisotropic and isotropic 3DEM training
datasets. To evaluate the effectiveness of the proposed method, we conducted
experiments using three 3DEM datasets acquired from brain. The experimental
results demonstrated that our proposed framework could successfully reconstruct
isotropic 3DEM from the anisotropic acquisition.