scholarly journals NanoJ: a high-performance open-source super-resolution microscopy toolbox

2018 ◽  
Author(s):  
Romain F. Laine ◽  
Kalina L. Tosheva ◽  
Nils Gustafsson ◽  
Robert D. M. Gray ◽  
Pedro Almada ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
High Performance ◽  
Image Quality Assessment ◽  
Fluorescent Microscopy ◽  
Super Resolution ◽  
Quantitative Information ◽  
Ease Of Use ◽  
Source Image ◽  
Super Resolution Microscopy

Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was de-veloped for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image re-construction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

scholarly journals NanoJ: a high-performance open-source super-resolution microscopy toolbox

2019 ◽  
Vol 52 (16) ◽  
pp. 163001 ◽  
Author(s):  
Romain F Laine ◽  
Kalina L Tosheva ◽  
Nils Gustafsson ◽  
Robert D M Gray ◽  
Pedro Almada ◽  
...  
Keyword(s):  
Open Source ◽  
High Performance ◽  
Super Resolution ◽  
Super Resolution Microscopy

scholarly journals VirusMapper: open-source nanoscale mapping of viral architecture through super-resolution microscopy

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Robert D. M. Gray ◽  
Corina Beerli ◽  
Pedro Matos Pereira ◽  
Kathrin Maria Scherer ◽  
Jerzy Samolej ◽  
...  
Keyword(s):  
Open Source ◽  
Super Resolution ◽  
Super Resolution Microscopy

scholarly journals EcoIP: An open source image analysis toolkit to identify different stages of plant phenology for multiple species with pan–tilt–zoom cameras

2013 ◽  
Vol 15 ◽  
pp. 58-65 ◽  
Author(s):  
Joel A. Granados ◽  
Eric A. Graham ◽  
Philippe Bonnet ◽  
Eric M. Yuen ◽  
Michael Hamilton
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Plant Phenology ◽  
Source Image ◽  
Multiple Species

scholarly journals Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects

2012 ◽  
Vol 160 (3) ◽  
pp. 1149-1159 ◽  
Author(s):  
Jonas De Vylder ◽  
Filip Vandenbussche ◽  
Yuming Hu ◽  
Wilfried Philips ◽  
Dominique Van Der Straeten
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Source Image ◽  
Analysis Tool ◽  
Automatic Quantification ◽  
Genotype Effects

scholarly journals Translation Microscopy (TRAM) for super-resolution imaging

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhen Qiu ◽  
Rhodri S Wilson ◽  
Yuewei Liu ◽  
Alison R Dun ◽  
Rebecca S Saleeb ◽  
...  
Keyword(s):  
Cell Biology ◽  
Super Resolution ◽  
Ground Truth ◽  
Image Plane ◽  
Ease Of Use ◽  
Stimulated Emission ◽  
Multiple Diffraction ◽  
Microscopy Technique ◽  
Resolution Imaging ◽  
Super Resolution Microscopy

Abstract Super-resolution microscopy is transforming our understanding of biology but accessibility is limited by its technical complexity, high costs and the requirement for bespoke sample preparation. We present a novel, simple and multi-color super-resolution microscopy technique, called translation microscopy (TRAM), in which a super-resolution image is restored from multiple diffraction-limited resolution observations using a conventional microscope whilst translating the sample in the image plane. TRAM can be implemented using any microscope, delivering up to 7-fold resolution improvement. We compare TRAM with other super-resolution imaging modalities, including gated stimulated emission deletion (gSTED) microscopy and atomic force microscopy (AFM). We further developed novel ‘ground-truth’ DNA origami nano-structures to characterize TRAM, as well as applying it to a multi-color dye-stained cellular sample to demonstrate its fidelity, ease of use and utility for cell biology.

scholarly journals Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform

2016 ◽  
Vol 22 (3) ◽  
pp. 238-249 ◽  
Author(s):  
Ioannis K. Moutsatsos ◽  
Imtiaz Hossain ◽  
Claudia Agarinis ◽  
Fred Harbinski ◽  
Yann Abraham ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
High Throughput Screening ◽  
High Performance ◽  
Heterogeneous Data ◽  
Scientific Data ◽  
Source Image ◽  
Integration System ◽  
Continuous Integration ◽  
Processing Platform

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an “off-the-shelf,” open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.

CellStress - open source image analysis program for single-cell analysis

2010 ◽  
Author(s):  
Maria Smedh ◽  
Caroline Beck ◽  
Kristin Sott ◽  
Mattias Goksör
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Open Source ◽  
Single Cell Analysis ◽  
Source Image ◽  
Cell Analysis ◽  
Analysis Program
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