Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy

TL;DR

This paper introduces a deep learning method that enhances volumetric fluorescence microscopy images by improving axial resolution and detail without needing paired high-resolution data or extensive training data.

Contribution

The authors develop an unsupervised deep learning approach using a cycle-consistent GAN that requires only a single 3D image stack for training, eliminating the need for matched high-res target images.

Findings

Enhances axial resolution in fluorescence microscopy

Restores suppressed details and reduces artifacts

Requires minimal training data, only a single 3D stack

Abstract

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution from inferior axial resolution compared to the lateral resolution. To address this problem, here we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target volume images, our method greatly reduces the effort to put into practice as the training of a network requires as little as a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in lateral image plane and low-resolution 2D images in the other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution, but also restores suppressed visual details between the imaging planes and removes imaging artifacts.