Guided-deconvolution for Correlative Light and Electron Microscopy
University of Jena（耶拿大学）；Leibniz Institute of Photonic Technology（莱布尼茨光子技术研究所）；Jena Center for Soft Matter（耶拿软物质中心）；Centre for Structural Systems Biology（结构系统生物学中心）；University of Hamburg（汉堡大学）；Biozentrum, University of Basel（巴塞尔大学生物中心）；Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute（莱布尼茨天然产物研究与感染生物学研究所——汉斯·克诺尔研究所）
Fengjiao Ma, Rainer Kaufmann, Jaroslaw Sedzicki, Zoltán Cseresnyés, Christoph Dehio, Stephanie Hoeppener, Marc Thilo Figge, Rainer Heintzmann
Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. It attempts to automatically assign fluorescence-labelled structures to details visible in the EM image to bridge the gaps in both resolution and specificity between the two imaging modes.
UNC Chapel Hill/BRIC（北卡罗来纳大学教堂山分校/生物医学影像中心）；Microsoft Research Cambridge（微软研究院剑桥）；University of Delaware（特拉华大学）
Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies. Image registration for correlative microscopy is quite challenging because it is a multi-modal, multi-scale and multi-dimensional registration problem. In this report, I introduce two methods of image registration for correlative microscopy. The first method is based on fiducials (beads). I generate landmarks from the fiducials and compute the similarity transformation matrix based on three pairs of nearest corresponding land…
Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3-way spatially constrained cycle-consistent adversarial networks. The restored…
Solving the inverse problem of microscopy deconvolution with a residual Beylkin-Coifman-Rokhlin neural network
Center for Advanced Systems Understanding (CASUS)（先进系统理解中心）；Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR)（德累斯顿-罗斯托克研究所）；Max Delbrück Center for Molecular Medicine in the Helmholtz Association（马克斯·德尔布吕克分子医学研究中心）；Institute of Medical Physics and Biophysics, Charite-Universitätsmedizin（医学物理与生物物理研究所）；Institute of Computer Science, University of Wrocław（沃林福大学计算机科学研究所）
Seoul National University College of Medicine（首尔大学医学院）；KAIST（韩国科学技术院）
Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored as a fast and high performance alternative. In this paper, we present a novel unsupervised deep neural network for blind deconvolution based on cycle consistency and PSF modeling layers. In contrast to the recent CNN approaches for similar problem, the explicit PSF modeling layers…
Idiap Research Institute（伊迪亚普研究所）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches. We deconvolved both synthetic and experimentally-acquired data, and achieved an improvement of image SNR of 1.00 dB on average, compared to other deconvo…
University of Michigan-Shanghai Jiao Tong University Joint Institute（上海交通大学密西根学院）；Shanghai Jiao Tong University（上海交通大学）
Fluorescence microscopy plays an important role in biomedical research. The depth-variant point spread function (PSF) of a fluorescence microscope produces low-quality images especially in the out-of-focus regions of thick specimens. Traditional deconvolution to restore the out-of-focus images is usually insufficient since a depth-invariant PSF is assumed. This article aims at handling fluorescence microscopy images by learning-based depth-variant PSF and reducing artifacts. We propose adaptive weighting depth-variant deconvolution (AWDVD) with defocus level prediction convolutional neural net…
Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperf…
Roche Innovation Center Munich（罗氏慕尼黑创新中心）；Technische Universität München（慕尼黑工业大学）；SagivTech LTD.（SagivTech有限公司）
A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumor biopsies. Different cell types tend to migrate into various tissue compartments and form variable distribution patterns. Drug development requires correlative analysis of various biomarkers in and between the tissue compartments. To enable that, tissue slides are manually annotated by expert pathologists. Manual annotation of tissue slides is a labor intensive, tedious and error-prone task. Automation of this annotation process can improve accuracy and cons…
Max Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）
In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution employs the point spread functions (PSF) of the different views to simultaneously fuse and deconvolve the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline. Here we show that MV deconvolution in 3D can finally be achieved in real-time by reslicing the acquired data and processing cross-sectional planes individually on t…
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Seoul National University College of Medicine（首尔大学医学院）
Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms. Recently, the convolutional neural network (CNN) approaches have been studied as a fast and high performance alternative. Unfortunately, the CNN approaches usually require matched high resolution images for supervised training. In this paper, we present a novel unsupervised cycle-consistent generative adversarial network (cycleGAN) with a linear blur kernel, w…
Université de Nantes（南特大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；l’institut du thorax（胸腔研究所）；CHU Nantes（南特大学附属医院）；SFR Santé（健康科学研究联合会）
Correlative microscopy aims at combining two or more modalities to gain more information than the one provided by one modality on the same biological structure. Registration is needed at different steps of correlative microscopies workflows. Biologists want to select the image content used for registration not to introduce bias in the correlation of unknown structures. Intensity-based methods might not allow this selection and might be too slow when the images are very large. We propose an approach based on point clouds created from selected content by the biologist. These point clouds may be …
A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy
Wenzhou Key Laboratory of Biomedical Imaging（温州生物医学成像重点实验室）；Zhejiang Key Laboratory of Soft Matter Biomedical Materials（浙江软物质生物医学材料重点实验室）；Wenzhou Institute, University of Chinese Academy of Sciences（中国科学院大学温州研究院）；Postgraduate Training Base Alliance of Wenzhou Medical University（温州医学院研究生联合培养基地联盟）
Institute of Imaging and Computer Vision, RWTH Aachen University（亚琛工业大学成像与计算机视觉研究所）
Recent developments in fluorescence microscopy allow capturing high-resolution 3D images over time for living model organisms. To be able to image even large specimens, techniques like multi-view light-sheet imaging record different orientations at each time point that can then be fused into a single high-quality volume. Based on measured point spread functions (PSF), deconvolution and content fusion are able to largely revert the inevitable degradation occurring during the imaging process. Classical multi-view deconvolution and fusion methods mainly use iterative procedures and content-based …
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能）；Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心-德国环境健康研究中心）；Georg-August-University Göttingen（哥廷根大学）；GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）
Idiap Research Institute（IDIAP研究所）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with high-resolution microscopes leads to unsharp image regions and makes depth localization and quantitative image interpretation difficult. Here, we present a method that improves the resolution of light microscopy images of such objects by locally estimating image distortion while jointly estimating object distance to the focal plane. Specifically, we estimat…
University of Cambridge（剑桥大学）；MRC Laboratory of Molecular Biology（MRC分子生物学实验室）
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function (PSF) of a light-sheet microscope is determined by the interaction between the excitation sheet and the detection objective PSF. First, we introduce a model of the image formation process that incorporates this interaction, therefore capturing the main characteristics of this imaging modality. Then, we formulate a variational model that accounts for the combination of Poisson a…
Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. The ill-posed nature of the problem requires adequate priors to arrive to a desirable solution. Recently, it has been shown that deep learning architectures can serve as an image generation prior during unsupervised blind deconvolution optimization, however often exhibiting a performance fluctuation even on a single image. We propose to use Wiener-deconvolution to guide the image generator during optimization by providing it a sharpened version of the blurry image using an auxiliary kern…
Max Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden（德累斯顿系统生物学中心）；Faculty of Computer Science, Technical University Dresden（德累斯顿工业大学计算机科学系）
Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the net…
Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
In this paper, we propose a new model to segment cells in phase contrast microscopy images. Cell images collected from the similar scenario share a similar background. Inspired by this, we separate cells from the background in images by formulating the problem as a low-rank and structured sparse matrix decomposition problem. Then, we propose the inverse diffraction pattern filtering method to further segment individual cells in the images. This is a deconvolution process that has a much lower computational complexity when compared to the other restoration methods. Experiments demonstrate the e…
In this article, two closed and convex sets for blind deconvolution problem are proposed. Most blurring functions in microscopy are symmetric with respect to the origin. Therefore, they do not modify the phase of the Fourier transform (FT) of the original image. As a result blurred image and the original image have the same FT phase. Therefore, the set of images with a prescribed FT phase can be used as a constraint set in blind deconvolution problems. Another convex set that can be used during the image reconstruction process is the epigraph set of Total Variation (TV) function. This set does…
Bayesian In Vivo Tracking of Synapses using Joint Poisson Deconvolution and Diffeomorphic Registration
Department of Biomedical Engineering, Johns Hopkins University（约翰霍普金斯大学生物医学工程系）；Department of Neuroscience, Johns Hopkins University（约翰霍普金斯大学神经科学系）；Kavli Neuroscience Discovery Institute, Johns Hopkins University（约翰霍普金斯大学Kavli神经科学发现研究所）；Data Science and AI Institute, Johns Hopkins University（约翰霍普金斯大学数据科学与人工智能研究所）；Department of Applied Mathematics and Statistics, Johns Hopkins University（约翰霍普金斯大学应用数学与统计学系）
The Institute of Cancer Research（癌症研究所）；University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire（考文垂与华威郡大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution decon…
Czech Academy of Sciences（捷克科学院）；Institute of Information Theory and Automation（信息理论与自动化研究所）；Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-…
In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity. A medical image could be corrupted by both intrinsic noise, due to the device limitations, and, by extrinsic signal perturbations during image acquisition. Nowadays, CAD deep learning applications pre-process images with image denoising models to reinforce learning and prediction. In this work, an innovative and lightweight deep multiscale convol…
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders
Georgia State University（佐治亚州立大学）；Riphah International University（里法国际大学）；Roots IVY（鲁茨艾维）；Arid Agriculture University（干旱农业大学）；University of Central Florida（中佛罗里达大学）
Self-Tuning Regularization for Image Scanning Microscopy
MaLGa Center, DIBRIS, University of Genoa（MaLGa中心，DIBRIS，热那亚大学）；MMS, Istituto Italiano di Tecnologia (IIT)（MMS，意大利技术研究院（IIT））；CSML, Istituto Italiano di Tecnologia (IIT)（CSML，意大利技术研究院（IIT））
Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Biomedical Engineering（慕尼黑生物医学工程研究所）；Massachusetts Institute of Technology（麻省理工学院）；University of Bern（伯尔尼大学）
