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

by   Mohammad khateri, et al.

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.


page 1

page 2

page 3

page 4

page 5

page 6


Deep learning-based super-resolution fluorescence microscopy on small datasets

Fluorescence microscopy has enabled a dramatic development in modern bio...

STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning

Fetal motion is unpredictable and rapid on the scale of conventional MR ...

Self-Supervised Fine-tuning for Image Enhancement of Super-Resolution Deep Neural Networks

While Deep Neural Networks (DNNs) trained for image and video super-reso...

20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction

High spatial and temporal resolution across the whole brain is essential...

Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers

The spatial resolution of images of living samples obtained by fluoresce...

Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography

At X-ray beamlines of synchrotron light sources, the achievable time-res...

Learning-based Framework for US Signals Super-resolution

We propose a novel deep-learning framework for super-resolution ultrasou...

Please sign up or login with your details

Forgot password? Click here to reset