scholarly journals Visualizing and quantifying data from timelapse imaging experiments

2021 ◽  
Author(s):  
Eike K. Mahlandt ◽  
Joachim Goedhart
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Data Repository ◽  
Imaging Data ◽  
Time Intervals ◽  
Online Data ◽  
Analysis Methods ◽  
Regular Time ◽  
Quantitative Understanding ◽  
Obvious Feature

AbstractOne obvious feature of life is that it is highly dynamic. The dynamics can be captured by movies that are made by acquiring images at regular time intervals, a method that is also known as timelapse imaging. Looking at movies is a great way to learn more about the dynamics in cells, tissue and organisms. However, science is different from Netflix, in that it aims for a quantitative understanding of the dynamics. The quantification is important for the comparison of dynamics and to study effects of perturbations. Here, we provide detailed processing and analysis methods that we commonly use to analyze and visualize our timelapse imaging data. All methods use freely available open-source software and use example data that is available from an online data repository. The step-by-step guides together with example data allow for fully reproducible workflows that can be modified and adjusted to visualize and quantify other data from timelapse imaging experiments.Abstract Figure

scholarly journals Vascular flow simulations using SimVascular and OpenFOAM

2021 ◽  
Author(s):  
Swetha Yogeswaran ◽  
Fei Liu
Keyword(s):  
Fluid Dynamics ◽  
Blood Flow ◽  
Open Source ◽  
Open Source Software ◽  
Flow Analysis ◽  
Coarctation Of The Aorta ◽  
Patient Specific ◽  
Specific Information ◽  
Imaging Data ◽  
Set Up

AbstractApplications of computational fluid dynamics (CFD) techniques to aid in the diagnosis and treatment of cardiovascular disease have entered the research domain in recent years, due to their ability to provide valuable patient-specific information without risks associated with highly invasive procedures. SimVascular [1] [2] is an open-source software which allows streamlined processing and CFD blood flow analysis of medical imaging data. OpenFOAM [3] is a proven open-source software which allows for versatile modeling of various fluid dynamics phenomena. In this study, both SimVascular and OpenFOAM simulations are set up with identical computational mesh, similar numerical schemes, boundary conditions, and material properties, to model blood flow in the coronary artery of a 10 year old patient with Coarctation of the Aorta (CoA) who underwent end-to-side anastomosis. Difference in the flow fields such as flow rate, pressure, vorticity, and wall shear stress between SimVascular and OpenFOAM are analyzed. Similar results are obtained in both simulations up to a certain model time, before the results become drastically different. Both the similarities and differences are documented and discussed.

scholarly journals OPEN SCIENCE, KNOWLEDGE SHARING AND REPRODUCIBILITY AS DRIVERS FOR THE ADOPTION OF FOSS4G IN ENVIRONMENTAL RESEARCH

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 107-110
Author(s):  
I. Iosifescu Enescu ◽  
G-K. Plattner ◽  
L. Bont ◽  
M. Fraefel ◽  
R. Meile ◽  
...  
Keyword(s):  
Knowledge Sharing ◽  
Open Source ◽  
Open Source Software ◽  
Academic Research ◽  
Open Science ◽  
Environmental Data ◽  
Environmental Research ◽  
Data Repository ◽  
User Requirement ◽  
Data Portal

<p><strong>Abstract.</strong> Support for open science is a highly relevant user requirement for the environmental data portal EnviDat. EnviDat, the institutional data portal and publication data repository of the Swiss Federal Research Institute WSL, actively implements the FAIR (Findability, Accessibility, Interoperability and Reusability) principles and provides a range of services in the area of research data management. Open science, with its requirements for improved knowledge sharing and reproducibility, is driving the adoption of free and open source software for geospatial (FOSS4G) in academic research. Open source software can play a key role in the proper documentation of data sets, processes and methodologies, because it supports the transparency of methods and the precise documentation of all steps needed to achieve the published results. EnviDat actively supports these activities to enhance its support for open science. With EnviDat, WSL contributes to the ongoing cultural evolution in research towards open science and opportunities for distant collaboration.</p>

scholarly journals DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research

2015 ◽  
Author(s):  
Andriy Fedorov ◽  
David Clunie ◽  
Ethan Ulrich ◽  
Christian Bauer ◽  
Andreas Wahle ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Open Source ◽  
Open Source Software ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Dicom Standard ◽  
Free Open Source Software ◽  
Pet Ct ◽  
Free Open Source

Background. Imaging biomarkers hold tremendous promise in the precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation motivate integration of the clinical and imaging data, and the use of standardized approaches to sharing analysis results and semantics. We develop the methodology and supporting tools to perform these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging Communication in Medicine (DICOM®) international standard and free open source software tools. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor and reference regions of interest (ROI) using manual and semi-automatic approaches, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of encoding via new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited to the QIN-HEADNECK collection of The Cancer Imaging Archive. Supporting tools for data analysis and DICOM conversion are available as free open source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that DICOM standard can be used to represent various types of the analysis results and encode their complex relationships. As a result, the data objects are interoperable with a variety of readily available tools and toolkits, as well as commercial clinical imaging and analysis systems that adopt the DICOM standard virtually universally.

scholarly journals Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefania Alexandra Iakab ◽  
Lluc Sementé ◽  
María García-Altares ◽  
Xavier Correig ◽  
Pere Ràfols
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Open Source Software ◽  
Multimodal Imaging ◽  
Mass Spectrometry Imaging ◽  
Raman Imaging ◽  
Imaging Analysis ◽  
Imaging Data ◽  
Data Format ◽  
Computational Tools

Abstract Background Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand. Results Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications. Conclusion Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.

scholarly journals DICOM for quantitative imaging biomarker development: A standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research

2016 ◽  
Author(s):  
Andriy Fedorov ◽  
David Clunie ◽  
Ethan Ulrich ◽  
Christian Bauer ◽  
Andreas Wahle ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Open Source ◽  
Open Source Software ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Dicom Standard ◽  
Free Open Source Software ◽  
Pet Ct ◽  
Free Open Source

Background. Imaging biomarkers hold tremendous promise in the precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation motivate integration of the clinical and imaging data, and the use of standardized approaches to sharing analysis results and semantics. We develop the methodology and supporting tools to perform these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM®) international standard and free open source software tools. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor and reference regions of interest using manual and semi-automatic approaches, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results. A number of correction proposals to the standard were developed. The open source DICOM toolkit (DCMTK) was improved to simplify the task of encoding via new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited to the QIN-HEADNECK collection of The Cancer Imaging Archive. Supporting tools for data analysis and DICOM conversion were made available as free open source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that DICOM standard can be used to represent various types of the analysis results and encode their complex relationships. The resulting annotated objects are amenable for data mining applications, and are interoperable with a variety of systems that adopt the DICOM standard.

scholarly journals DICOM for quantitative imaging biomarker development: A standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research

2016 ◽  
Author(s):  
Andriy Fedorov ◽  
David Clunie ◽  
Ethan Ulrich ◽  
Christian Bauer ◽  
Andreas Wahle ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Open Source ◽  
Open Source Software ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Dicom Standard ◽  
Free Open Source Software ◽  
Pet Ct ◽  
Free Open Source

Background. Imaging biomarkers hold tremendous promise in the precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation motivate integration of the clinical and imaging data, and the use of standardized approaches to sharing analysis results and semantics. We develop the methodology and supporting tools to perform these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM®) international standard and free open source software tools. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor and reference regions of interest using manual and semi-automatic approaches, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results. A number of correction proposals to the standard were developed. The open source DICOM toolkit (DCMTK) was improved to simplify the task of encoding via new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited to the QIN-HEADNECK collection of The Cancer Imaging Archive. Supporting tools for data analysis and DICOM conversion were made available as free open source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that DICOM standard can be used to represent various types of the analysis results and encode their complex relationships. The resulting annotated objects are amenable for data mining applications, and are interoperable with a variety of systems that adopt the DICOM standard.

scholarly journals Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexandre Routier ◽  
Ninon Burgos ◽  
Mauricio Díaz ◽  
Michael Bacci ◽  
Simona Bottani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Scalar Fields ◽  
Machine Learning Algorithms ◽  
Software Platform ◽  
Imaging Data ◽  
Clinical Neuroscience ◽  
Neuroimaging Data ◽  
Share Data

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Vascular flow simulations using SimVascular and OpenFOAM (Preprint)

2021 ◽  
Author(s):  
Swetha Yogeswaran ◽  
Fei Liu
Keyword(s):  
Fluid Dynamics ◽  
Blood Flow ◽  
Open Source ◽  
Open Source Software ◽  
Flow Analysis ◽  
Coarctation Of The Aorta ◽  
Patient Specific ◽  
Specific Information ◽  
Imaging Data ◽  
Set Up

UNSTRUCTURED Applications of computational fluid dynamics (CFD) techniques to aid in the diagnosis and treatment of cardiovascular disease have entered the research domain in recent years, due to their ability to provide valuable patient-specific information without risks associated with highly invasive procedures. SimVascular is an open-source software which allows streamlined processing and CFD blood flow analysis of medical imaging data. OpenFOAM is a proven open-source software which allows for versatile modeling of various fluid dynamics phenomena. In this study, both SimVascular and OpenFOAM simulations are set up with identical computational mesh, similar numerical schemes, boundary conditions, and material properties, to model blood flow in the coronary artery of a 10 year old patient with Coarctation of the Aorta (CoA) who underwent end-to-side anastomosis. Difference in the flow fields such as flow rate, pressure, vorticity, and wall shear stress between SimVascular and OpenFOAM are analyzed. Similar results are obtained in both simulations up to a certain model time, before the results become drastically different. Both the similarities and differences are documented and discussed.

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