scholarly journals ZELDA: A 3D Image Segmentation and Parent-Child Relation Plugin for Microscopy Image Analysis in napari

2022 ◽  
Vol 3 ◽  
Author(s):  
Rocco D’Antuono ◽  
Giuseppina Pisignano
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Cell Number ◽  
Cell Segmentation ◽  
Data Sets ◽  
3D Analysis ◽  
3D Segmentation ◽  
Analysis Workflow ◽  
Parent Child

Bioimage analysis workflows allow the measurement of sample properties such as fluorescence intensity and polarization, cell number, and vesicles distribution, but often require the integration of multiple software tools. Furthermore, it is increasingly appreciated that to overcome the limitations of the 2D-view-based image analysis approaches and to correctly understand and interpret biological processes, a 3D segmentation of microscopy data sets becomes imperative. Despite the availability of numerous algorithms for the 2D and 3D segmentation, the latter still offers some challenges for the end-users, who often do not have either an extensive knowledge of the existing software or coding skills to link the output of multiple tools. While several commercial packages are available on the market, fewer are the open-source solutions able to execute a complete 3D analysis workflow. Here we present ZELDA, a new napari plugin that easily integrates the cutting-edge solutions offered by python ecosystem, such as scikit-image for image segmentation, matplotlib for data visualization, and napari multi-dimensional image viewer for 3D rendering. This plugin aims to provide interactive and zero-scripting customizable workflows for cell segmentation, vesicles counting, parent-child relation between objects, signal quantification, and results presentation; all included in the same open-source napari viewer, and “few clicks away”.

scholarly journals ZELDA: a 3D Image Segmentation and Parent-Child relation plugin for microscopy image analysis in napari

2021 ◽  
Author(s):  
Rocco D'Antuono ◽  
Giuseppina Pisignano
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Cell Number ◽  
Cell Segmentation ◽  
Data Sets ◽  
3D Analysis ◽  
3D Segmentation ◽  
Analysis Workflow ◽  
Parent Child

Bioimage analysis workflows allow the measurement of sample properties such as fluorescence intensity and polarization, cell number, and vesicles distribution, but often require the integration of multiple software tools. Furthermore, it is increasingly appreciated that to overcome the limitations of the 2D-view-based image analysis approaches and to correctly understand and interpret biological processes, a 3D segmentation of microscopy data sets becomes imperative. Despite the availability of numerous algorithms for the 2D and 3D segmentation, the latter still offers some challenges for the end-users, who often do not have either an extensive knowledge of the existing software or coding skills to link the output of multiple tools. While several commercial packages are available on the market, fewer are the open-source solutions able to execute a complete 3D analysis workflow. Here we present ZELDA, a new napari plugin that easily integrates the cutting-edge solutions offered by python ecosystem, such as scikit-image for image segmentation, matplotlib for data visualization, and napari multi-dimensional image viewer for 3D rendering. This plugin aims to provide interactive and zero-scripting customizable workflows for cell segmentation, vesicles counting, parent-child relation between objects, signal quantification, and results presentation; all included in the same open-source napari viewer, and 'few clicks away'.

scholarly journals The Allen Cell and Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images

2018 ◽  
Author(s):  
Jianxu Chen ◽  
Liya Ding ◽  
Matheus P. Viana ◽  
HyeonWoo Lee ◽  
M. Filip Sluezwski ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Open Source ◽  
Fluorescence Microscope ◽  
3D Segmentation ◽  
Lamin B1 ◽  
Microscope Images ◽  
Microscopy Images ◽  
Intracellular Structure

A continuing challenge in quantitative cell biology is the accurate and robust 3D segmentation of structures of interest from fluorescence microscopy images in an automated, reproducible, and widely accessible manner for subsequent interpretable data analysis. We describe the Allen Cell and Structure Segmenter (Segmenter), a Python-based open source toolkit developed for 3D segmentation of cells and intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high-replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derivedcardiomyocytes. The Segmenter consists of two complementary elements, a classic image segmentation workflow with a restricted set of algorithms and parameters and an iterative deep learning segmentation workflow. We created a collection of 20 classic image segmentation workflows based on 20 distinct and representative intracellular structure localization patterns as a "lookup table" reference and starting point for users. The iterative deep learning workflow can take over when the classic segmentation workflow is insufficient. Two straightforward "human-in-the loop" curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need for manual painting in 3D. The deep learning model architectures used in this toolkit were designed and tested specifically for 3D fluorescence microscope images and implemented as readable scripts. The Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher. We include two useful applications to demonstrate how we used the classic image segmentation and iterative deep learning workflows to solve more challenging 3D segmentation tasks. First, we introduce the "Training Assay" approach, a new experimental-computational co-design concept to generate more biologically accurate segmentation ground truths. We combined the iterative deep learning workflow with three Training Assays to develop a robust, scalable cell and nuclear instance segmentation algorithm, which could achieve accurate target segmentation for over 98% of individual cells and over 80% of entire fields of view. Second, we demonstrate how to extend the lamin B1 segmentation model built from the iterative deep learning workflow to obtain more biologically accurate lamin B1 segmentation by utilizing multi-channel inputs and combining multiple ML models. The steps and workflows used to develop these algorithms are generalizable to other similar segmentation challenges. More information, including tutorials and code repositories, are available at allencell.org/segmenter.

scholarly journals CellTool: an open source software combining bio-image analysis and mathematical modeling

2018 ◽  
Author(s):  
Georgi Danovski ◽  
Teodora Dyankova ◽  
Stoyno Stoynov
Keyword(s):  
Mathematical Modeling ◽  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Time Lapse ◽  
Supplementary Information ◽  
Data Sets ◽  
Link Type ◽  
Sample Data ◽  
Supplementary Material

AbstractSummaryWe present CellTool, a stand-alone open source software with a Graphical User Interface for image analysis, optimized for measurement of time-lapse microscopy images. It combines data management, image processing, mathematical modeling and graphical presentation of data in a single package. Multiple image filters, segmentation and particle tracking algorithms, combined with direct visualization of the obtained results make CellTool an ideal application for rapid execution of complex tasks. In addition, the software allows for the fitting of the obtained results to predefined or custom mathematical models. Importantly, CellTool provides a platform for easy implementation of custom image analysis packages written on a variety of programing languages.Availability and ImplementationCellTool is a free software available for MS Windows OS under the terms of the GNU General Public License. Executables and source files, supplementary information and sample data sets are freely available for download at URL: https://dnarepair.bas.bg/software/CellTool/[email protected]; [email protected];Supplementary informationSupplementary data are available at URL: https://dnarepair.bas.bg/software/CellTool/Program/CellTool_UserGuide.pdf

scholarly journals MatVTK - 3D Visualization for Matlab

2009 ◽  
Author(s):  
Erich Birngruber ◽  
René Donner ◽  
Georg Langs
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Medical Image ◽  
3D Visualization ◽  
Medical Image Analysis ◽  
Medical Data ◽  
Data Sets ◽  
Steep Learning Curve ◽  
Visualization Toolkit ◽  
Crucial Part

The rapid and flexible visualization of large amounts of com- plex data has become a crucial part in medical image analysis. In re- cent years the Visualization Toolkit (VTK) has evolved as the de-facto standard for open-source medical data visualization. It features a clean design based on a data flow paradigm, which the existing wrappers for VTK (Python, Tcl/Tk, Simulink) closely follow. This allows to elegantly model many types of algorithms, but presents a steep learning curve for beginners. In contrast to existing approaches we propose a framework for accessing VTK’s capabilities from within MATLAB, using a syntax which closely follows MATLAB’s graphics primitives. While providing users with the advanced, fast 3D visualization capabilities MATLAB does not provide, it is easy to learn while being flexible enough to allow for complex plots, large amounts of data and combinations of visualiza- tions. The proposed framework will be made available as open source with detailed documentation and example data sets.

scholarly journals ITK Tutorial Presentation Slides

2006 ◽  
Author(s):  
Luis Ibanez ◽  
Lydia Ng ◽  
Josh Cates ◽  
Stephen Aylward ◽  
Bill Lorensen ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Data Representation ◽  
Medical Image Segmentation ◽  
Object Oriented Programming ◽  
Application Development

This course introduces attendees to select open-source efforts in the field of medical image analysis. Opportunities for users and developers are presented. The course particularly focuses on the open-source Insight Toolkit (ITK) for medical image segmentation and registration. The course describes the procedure for downloading and installing the toolkit and covers the use of its data representation and filtering classes. Attendees are shown how ITK can be used in their research, rapid prototyping, and application development.LEARNING OUTCOMES After completing this course, attendees will be able to: contribute to and benefit from open-source software for medical image analysis download and install the ITK toolkit start their own software project based on ITK design and construct an image processing pipeline combine ITK filters for medical image segmentation combine ITK components for medical image registrationINTENDED AUDIENCE This course is intended for anyone involved in medical image analysis. In particular it targets graduate students, researchers and professionals in the areas of computer science and medicine. Attendees should have an intermediate level on object oriented programming with C++ and must be familiar with the basics of medical image processing and analysis.

scholarly journals Micro–X-ray fluorescence (µXRF) analysis of proximal impactites: High-resolution element mapping, digital image analysis, and quantifications

2021 ◽  
Author(s):  
Pim Kaskes ◽  
Thomas Déhais ◽  
Sietze J. de Graaff ◽  
Steven Goderis ◽  
Philippe Claeys
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Trace Element ◽  
Hypervelocity Impact ◽  
Data Sets ◽  
X Ray ◽  
Distribution Maps ◽  
Complex Processes ◽  
Analysis Workflow ◽  
Element Mapping

ABSTRACT Quantitative insights into the geochemistry and petrology of proximal impactites are fundamental to understand the complex processes that affected target lithologies during and after hypervelocity impact events. Traditional analytical techniques used to obtain major- and trace-element data sets focus predominantly on either destructive whole-rock analysis or laboratory-intensive phase-specific micro-analysis. Here, we present micro–X-ray fluorescence (µXRF) as a state-of-the-art, time-efficient, and nondestructive alternative for major- and trace-element analysis for both small and large samples (up to 20 cm wide) of proximal impactites. We applied µXRF element mapping on 44 samples from the Chicxulub, Popigai, and Ries impact structures, including impact breccias, impact melt rocks, and shocked target lithologies. The µXRF mapping required limited to no sample preparation and rapidly generated high-resolution major- and trace-element maps (~1 h for 8 cm2, with a spatial resolution of 25 µm). These chemical distribution maps can be used as qualitative multi-element maps, as semiquantitative single-element heat maps, and as a basis for a novel image analysis workflow quantifying the modal abundance, size, shape, and degree of sorting of segmented components. The standardless fundamental parameters method was used to quantify the µXRF maps, and the results were compared with bulk powder techniques. Concentrations of most major elements (Na2O–CaO) were found to be accurate within 10% for thick sections. Overall, we demonstrate that µXRF is more than only a screening tool for heterogeneous impactites, because it rapidly produces bulk and phase-specific geochemical data sets that are suitable for various applications within the earth sciences.

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

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