scholarly journals A Novel 3-Dimensional technique in measuring pericoronary epicardial adipose tissue radiodensity

2020 ◽  
Author(s):  
Lingyu Xu ◽  
Stanislau Hrybouski ◽  
Yuancheng Xu ◽  
Richard Coulden ◽  
Emer Sonnex ◽  
...  
Keyword(s):  
Image Analysis ◽  
Adipose Tissue ◽  
Open Source ◽  
Open Source Software ◽  
Epicardial Adipose Tissue ◽  
Full Length ◽  
Imaging Biomarker ◽  
List Type ◽  
Similarity Coefficients ◽  
Analysis Pipeline

ABSTRACTObjectivesThis study aimed to investigate a novel semi-automated three-dimensional (3D) quantification of the pericoronary epicardial adipose tissue radiodensity (PCATrd).MethodsTwenty-four subjects who previously underwent contrast-enhanced cardiac CT scans were retrospectively identified. The PCATrd was measured in ITK-SNAP imaging software using a Hounsfield unit threshold (−190,-3) to define epicardial adipose tissue (EAT). A spherical 3D brush tool was used on multiplanar reformatted images to segment the PCAT. We defined the PCATrd as EAT within the orthogonal distance from the coronary artery (CA) outer wall equal to the diameter of the corresponding CA segment. The segmentation followed the path of major CAs. Additionally, the PCAT of twenty-five calcified segments were segmented. Reliability of this novel segmentation protocol was assessed using Dice Similarity Coefficients (DSCs) and intraclass coefficient (ICC).ResultsThe segmentation reproducibility for the PCAT was high, with intraobserver DSC 0.86±0.04 for the full length of major CAs and 0.85±0.07 for the calcified segments, and interobserver DSC 0.84±0.04 for the full length of major CAs and 0.83±0.05 for the calcified segments. The reproducibility of the PCATrd value assessed by ICC was also excellent, with intraobserver ICC 0.99 for the full length of major CAs and 0.99 for the calcified segments, and interobserver ICC 0.99 for the full length of major CAs and 0.99 for the calcified segments.ConclusionsOur novel 3D PCATrd quantification technique is reliable and reproducible. The availability of the open source software and detailed image analysis pipeline will enable reliable replications and broad uptake of our technique.Key pointsWe have produced a novel, semiautomated technique to comprehensively quantify pericoronary epicardial adipose tissue radiodensity (PCATrd) which is a novel imaging biomarker of coronary inflammation.Our method of PCAT segmentation has excellent reproducibility.We use open source software and provide detailed image analysis pipeline of quantifying PCATrd, which will allow easy replication and broad uptake of our technique.

scholarly journals AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Plant Methods ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Michael P. Pound ◽  
Susan Fozard ◽  
Mercedes Torres Torres ◽  
Brian G. Forde ◽  
Andrew P. French
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Analysis Approach ◽  
Plant Phenotyping

scholarly journals QuPath: Open source software for digital pathology image analysis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Peter Bankhead ◽  
Maurice B. Loughrey ◽  
José A. Fernández ◽  
Yvonne Dombrowski ◽  
Darragh G. McArt ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Digital Pathology ◽  
Pathology Image

scholarly journals IOCBIO Kinetics: An open-source software solution for analysis of data traces

2020 ◽  
Vol 16 (12) ◽  
pp. e1008475
Author(s):  
Marko Vendelin ◽  
Martin Laasmaa ◽  
Mari Kalda ◽  
Jelena Branovets ◽  
Niina Karro ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Data Entry ◽  
Routine Data ◽  
Analysis Pipeline ◽  
Web Interfaces ◽  
Time Space ◽  
Recorded Data ◽  
A Site ◽  
Data Analysis Pipeline

Biological measurements frequently involve measuring parameters as a function of time, space, or frequency. Later, during the analysis phase of the study, the researcher splits the recorded data trace into smaller sections, analyzes each section separately by finding a mean or fitting against a specified function, and uses the analysis results in the study. Here, we present the software that allows to analyze these data traces in a manner that ensures repeatability of the analysis and simplifies the application of FAIR (findability, accessibility, interoperability, and reusability) principles in such studies. At the same time, it simplifies the routine data analysis pipeline and gives access to a fast overview of the analysis results. For that, the software supports reading the raw data, processing the data as specified in the protocol, and storing all intermediate results in the laboratory database. The software can be extended by study- or hardware-specific modules to provide the required data import and analysis facilities. To simplify the development of the data entry web interfaces, that can be used to enter data describing the experiments, we released a web framework with an example implementation of such a site. The software is covered by open-source license and is available through several online channels.

scholarly journals Object Based Land Cover Extraction Using Open Source Software

2013 ◽  
Vol 12 ◽  
pp. 26-30
Author(s):  
Abhasha Joshi ◽  
Janak Raj Joshi ◽  
Nawaraj Shrestha ◽  
Saroj Shrestha ◽  
Sudarshan Gautam
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Open Source ◽  
Open Source Software ◽  
Contextual Information ◽  
Region Growing ◽  
Classification Method ◽  
Object Based ◽  
High Resolution Satellite Imagery ◽  
Best Parameter

Land cover is observed bio-physical cover of the earth’s surface and is an important resource for global monitoring studies, resource management, and planning activities. Traditionally these land resources were obtained from imagery using pixel based image analysis. But with the advent of High resolution satellite imagery and computation techniques these data are now widely being prepared using Object based Image Analysis (OBIA) techniques. But mostly only algorithm provided in commercial software and Ecognition in particular is being used to study OBIA. This paper aims to assess the application of an open source software Spring for OBIA. In this Study 0.5 meter pan sharpened Geo-Eye image was classified using spring software. The image was first segmented using region growing algorithm with similarity and area parameter. Using hit and trail method best parameter for segmentation for the study area was found. These objects were subsequently classified using Bhattacharya Distance. In this classification method spectral derivatives of the segment such as mean, median, standard deviation etc. were used which make this method useful. However the shape, size and context of the segment can’t be accounted during classification. i.e. rule based classification is not possible in spring. This classification method provides satisfactory overall accuracy of 78.46% with kappa coefficient 0.74. This classification method gave smooth land cover classes without salt and pepper effect and good appearance of land cover classes. However image segmentation and classification based on additional parameters such as shape and size of the segment, contextual information, pixel topology etc may give better classification result. Nepalese Journal on Geoinformatics -12, 2070 (2013AD): 26-30

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 Evaluation of p53 Immunohistochemical Expression Using Open-Source Software for Digital Image Analysis: A Tissue Microarray Study of Penile Squamous Cell Carcinomas

2015 ◽  
Author(s):  
Alcides Chaux ◽  
George J Netto ◽  
Authur L Burnett
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Squamous Cell Carcinomas ◽  
Digital Analysis ◽  
Visual Estimation ◽  
Immunohistochemical Expression ◽  
Software Packages

The addition of molecular biomarkers is needed to increase the accuracy of pathologic factors as prognosticators of outcome in penile squamous cell carcinomas (SCC). Evaluation of these biomarkers is usually carried out by immunohistochemistry. Herein we assess p53 immunohistochemical expression on tissue samples of penile SCC using freely-available, open-source software packages for digital image analysis. We also compared the results of digital analysis with standard visual estimation. Percentages of p53 positive cells were higher by visual estimation than by digital analysis. However, correlation was high between both methods. Our study shows that evaluation of p53 immunohistochemical expression is feasible using open-source software packages for digital image analysis. Although our analysis was limited to penile SCC, the rationale should also hold for other tumor types in which evaluation of p53 immunohistochemical expression is required. This approach would reduce interobserver variability, and would provide a standardized method for reporting the results of immunohistochemical stains. As these diagnostic tools are freely-available online, researchers and practicing pathologists could incorporate them in their daily practice without increasing diagnostic costs.

Sign in / Sign up

Export Citation Format

Share Document