Dataset Information

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

ABSTRACT: Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations to show that applying the trained 'de-aberration' networks outperforms alternative methods, and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

SUBMITTER: Guo M

PROVIDER: S-EPMC10659418 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Guo Min M Wu Yicong Y Hobson Chad M CM Su Yijun Y Qian Shuhao S Krueger Eric E Christensen Ryan R Kroeschell Grant G Bui Johnny J Chaw Matthew M Zhang Lixia L Liu Jiamin J Hou Xuekai X Han Xiaofei X Lu Zhiye Z Ma Xuefei X Zhovmer Alexander A Combs Christian C Moyle Mark M Yemini Eviatar E Liu Huafeng H Liu Zhiyi Z Benedetto Alexandre A La Riviere Patrick P Colón-Ramos Daniel D Shroff Hari H

bioRxiv : the preprint server for biology 20240715

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (i ...[more]

PMID: 37986950

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Project description:MotivationSuper-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.ResultsHere, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster.Availability and implementationThe main program is available at https://github.com/lykaust15/DLBI.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Publications

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

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