scholarly journals Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Marcel Müller ◽  
Viola Mönkemöller ◽  
Simon Hennig ◽  
Wolfgang Hübner ◽  
Thomas Huser
Keyword(s):  
Image Reconstruction ◽  
Open Source ◽  
Super Resolution ◽  
Source Image ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Microscopy Data

scholarly journals Imaging tissues and cells beyond the diffraction limit with structured illumination microscopy and Bayesian image reconstruction

2018 ◽  
Author(s):  
Jakub Pospíšil ◽  
Tomáš Lukeš ◽  
Justin Bendesky ◽  
Karel Fliegel ◽  
Kathrin Spendier ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Fluorescent Protein ◽  
Super Resolution ◽  
Live Cells ◽  
Structured Illumination ◽  
Optical Sectioning ◽  
Structured Illumination Microscopy ◽  
Raw Data ◽  
Bayesian Image Reconstruction

AbstractBackgroundStructured illumination microscopy (SIM) is a family of methods in optical fluorescence microscopy that can achieve both optical sectioning and super-resolution effects. SIM is a valuable method for high resolution imaging of fixed cells or tissues labeled with conventional fluorophores, as well as for imaging the dynamics of live cells expressing fluorescent protein constructs. In SIM, one acquires a set of images with shifting illumination patterns. This set of images is subsequently treated with image analysis algorithms to produce an image with reduced out-of-focus light (optical sectioning) and/or with improved resolution (super-resolution).FindingsFive complete and freely available SIM datasets are presented including raw and analyzed data. We report methods for image acquisition and analysis using open source software along with examples of the resulting images when processed with different methods. We processed the data using established optical sectioning SIM and super-resolution SIM methods, and with newer Bayesian restoration approaches which we are developing.ConclusionVarious methods for SIM data acquisition and processing are actively being developed, but complete raw data from SIM experiments is not typically published. Publicly available, high quality raw data with examples of processed results will aid researchers when developing new methods in SIM. Biologists will also find interest in the high-resolution images of animal tissues and cells we acquired. All of the data was processed with SIMToolbox, an open source and freely available software solution for SIM.

scholarly journals Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Christian Pilger ◽  
Jakub Pospíšil ◽  
Marcel Müller ◽  
Martin Ruoff ◽  
Martin Schütte ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Super Resolution ◽  
Image Resolution ◽  
Laser Scanning Microscopy ◽  
Tissue Imaging ◽  
Source Image ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Fluorescence Excitation ◽  
Two Photon

Fluorescence-based microscopy as one of the standard tools in biomedical research benefits more and more from super-resolution methods, which offer enhanced spatial resolution allowing insights into new biological processes. A typical drawback of using these methods is the need for new, complex optical set-ups. This becomes even more significant when using two-photon fluorescence excitation, which offers deep tissue imaging and excellent z-sectioning. We show that the generation of striped-illumination patterns in two-photon laser scanning microscopy can readily be exploited for achieving optical super-resolution and contrast enhancement using open-source image reconstruction software. The special appeal of this approach is that even in the case of a commercial two-photon laser scanning microscope no optomechanical modifications are required to achieve this modality. Modifying the scanning software with a custom-written macro to address the scanning mirrors in combination with rapid intensity switching by an electro-optic modulator is sufficient to accomplish the acquisition of two-photon striped-illumination patterns on an sCMOS camera. We demonstrate and analyse the resulting resolution improvement by applying different recently published image resolution evaluation procedures to the reconstructed filtered widefield and super-resolved images. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)'.

scholarly journals mmSIM: an open toolbox for accessible structured illumination microscopy

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Craig T. Russell ◽  
Michael Shaw
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
High Speed ◽  
Super Resolution ◽  
Software Tools ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Light Modulator ◽  
Wide Range ◽  
Hardware Designs

Since the first practical super-resolution structured illumination fluorescence microscopes (SIM) were demonstrated more than two decades ago, the method has become increasingly popular for a wide range of bioimaging applications. The high cost and relative inflexibility of commercial systems, coupled with the conceptual simplicity of the approach and the desire to exploit and customize existing hardware, have led to the development of a large number of home-built systems. Several detailed hardware designs are available in the scientific literature, complemented by open-source software tools for SIM image validation and reconstruction. However, there remains a lack of simple open-source software to control these systems and manage the synchronization between hardware components, which is critical for effective SIM imaging. This article describes a new suite of software tools based on the popular Micro-Manager package, which enable the keen microscopist to develop and run a SIM system. We use the software to control two custom-built, high-speed, spatial light modulator-based SIM systems, evaluating their performance by imaging a range of fluorescent samples. By simplifying the process of SIM hardware development, we aim to support wider adoption of the technique. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

scholarly journals Method of lateral image reconstruction in structured illumination microscopy with super resolution

2016 ◽  
Vol 09 (03) ◽  
pp. 1630002 ◽  
Author(s):  
Qiang Yang ◽  
Liangcai Cao ◽  
Hua Zhang ◽  
Hao Zhang ◽  
Guofan Jin
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Parameters Estimation ◽  
Gaussian Beams ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Reconstruction Process ◽  
Correlation Algorithm ◽  
Auto Correlation ◽  
Enhanced Resolution

The image reconstruction process in super-resolution structured illumination microscopy (SIM) is investigated. The structured pattern is generated by the interference of two Gaussian beams to encode undetectable spectra into detectable region of microscope. After parameters estimation of the structured pattern, the encoded spectra are computationally decoded and recombined in Fourier domain to equivalently increase the cut-off frequency of microscope, resulting in the extension of detectable spectra and a reconstructed image with about two-fold enhanced resolution. Three different methods to estimate the initial phase of structured pattern are compared, verifying the auto-correlation algorithm affords the fast, most precise and robust measurement. The artifacts sources and detailed reconstruction flowchart for both linear and nonlinear SIM are also presented.

scholarly journals mmSIM: An open toolbox for accessible structured illumination microscopy

2021 ◽  
Author(s):  
Craig T. Russell ◽  
Michael Shaw
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
High Speed ◽  
Super Resolution ◽  
Software Tools ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Light Modulator ◽  
Wide Range ◽  
Hardware Designs

SummarySince the first practical super-resolution structured illumination fluorescence microscopes (SIM) were demonstrated more than two decades ago the method has become increasingly popular for a wide range of bioimaging applications. The high cost and relative inflexibility of commercial systems, coupled with the conceptual simplicity of the approach and the desire to exploit and customise existing hardware, have led to the development of a large number of home-built systems. Several detailed hardware designs are available in the scientific literature, complemented by open-source software tools for SIM image validation and reconstruction. However, there remains a lack of simple open-source software to control these systems and manage the synchronization between hardware components, which is critical for effective SIM imaging. This article describes a new suite of software tools based on the popular Micro-Manager package, which enable the keen microscopist to develop and run a SIM system. We use the software to control two custom-built, high-speed, spatial light modulator-based SIM systems, evaluating their performance by imaging a range of fluorescent samples. By simplifying the process of SIM hardware development, we aim to support wider adoption of the technique.

scholarly journals UCsim2: 2D Structured Illumination Microscopy using UC2

2021 ◽  
Author(s):  
Haoran Wang ◽  
Réne Lachmann ◽  
Barbora Marsikova ◽  
Rainer Heinzmann ◽  
Benedict Diederich
Keyword(s):  
Open Source ◽  
Super Resolution ◽  
Structured Illumination ◽  
Optical Sectioning ◽  
Structured Illumination Microscopy ◽  
Manufacturing Tolerance ◽  
Resolution Imaging ◽  
Open Source Hardware ◽  
Microscopy Techniques ◽  
Diffraction Barrier

State-of-the-art microscopy techniques enable the imaging of sub-diffraction barrier biological structures at the price of high-costs or lacking transparency. We try to reduce some of these barriers by presenting a super-resolution upgrade to our recently presented open-source optical toolbox UC2. Our new injection moulded parts allow larger builds with higher precision. The 4x lower manufacturing tolerance compared to 3D printing makes assemblies more reproducible. By adding consumer-grade available open-source hardware such as digital mirror devices (DMD) and laser projectors we demonstrate a compact 3D multimodal setup that combines image scanning microscopy (ISM) and structured illumination microscopy (SIM). We demonstrate a gain in resolution and optical sectioning using the two different modes compared to the widefield limit by imaging Alexa Fluor 647- and SiR-stained HeLa cells. We compare different objective lenses and by sharing the designs and manuals of our setup, we make super-resolution imaging available to everyone.

scholarly journals Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

2020 ◽  
Author(s):  
Zafran Hussain Shah ◽  
Marcel Müller ◽  
Tung-Cheng Wang ◽  
Philip Maurice Scheidig ◽  
Axel Schneider ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Image Data ◽  
Excitation Power ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Quality ◽  
Reconstruction Methods ◽  
High Modulation

AbstractSuper-resolution structured illumination microscopy (SR-SIM) provides an up to two-fold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data, e.g. as a result of low excitation power or low exposure times, result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high quality reconstructed images. A residual encoding-decoding convolution neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the entirely deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well over various noise levels. The combination of computational reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.

Sign in / Sign up

Export Citation Format

Share Document