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Three-dimensional electron microscopy (3DEM) is an essential technique toinvestigate volumetric tissue ultra-structure. Due to technical limitations andhigh imaging costs, samples are often imaged anisotropically, where resolutionin the axial direction ($z$) is lower than in the lateral directions $(x,y)$.This anisotropy 3DEM can hamper subsequent analysis and visualization tasks. Toovercome this limitation, we propose a novel deep-learning (DL)-basedself-supervised super-resolution approach that computationally reconstructsisotropic 3DEM from the anisotropic acquisition. The proposed DL-basedframework is built upon the U-shape architecture incorporatingvision-transformer (ViT) blocks, enabling high-capability learning of local andglobal multi-scale image dependencies. To train the tailored network, we employa self-supervised approach. Specifically, we generate pairs of anisotropic andisotropic training datasets from the given anisotropic 3DEM data. By feedingthe given anisotropic 3DEM dataset in the trained network through our proposedframework, the isotropic 3DEM is obtained. Importantly, this isotropicreconstruction approach relies solely on the given anisotropic 3DEM dataset anddoes not require pairs of co-registered anisotropic and isotropic 3DEM trainingdatasets. To evaluate the effectiveness of the proposed method, we conductedexperiments using three 3DEM datasets acquired from brain. The experimentalresults demonstrated that our proposed framework could successfully reconstructisotropic 3DEM from the anisotropic acquisition.

Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition