Modern Scientific Visualizations on the Web

Franke, Loraine; Haehn, Daniel

doi:10.3390/informatics7040037

Cited by 16 publications

(13 citation statements)

References 138 publications

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“…At the symposium, Ms. Loraine Franke presented her work on developing web-based interactive visualization tools for diffusion tractography imaging data (Franke & Haehn, 2020 ; Franke et al, 2020 ). Her open-source tool, FiberStars (Franke et al, 2020 ) (Fig.…”

Section: Section Ii: Machine Learning Applications For Volumetric Ima...mentioning

confidence: 99%

How Machine Learning is Powering Neuroimaging to Improve Brain Health

et al. 2022

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This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

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Section: Section Ii: Machine Learning Applications For Volumetric Ima...mentioning

confidence: 99%

How Machine Learning is Powering Neuroimaging to Improve Brain Health

et al. 2022

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“…The big data revolution is providing enormously large, incredibly rich, and highly complex data volumes that impose extremely challenging demands on traditional visualization approaches (see e.g. [9,14]). The demands to address are efficiency, i.e.…”

Section: Scientific Visualization Challengesmentioning

confidence: 99%

“…The NEANIAS 7 project emerges in this context as an ambitious research and innovation action specifically addressing the prototyping of new innovative services 8 for underwater, atmospheric and space research thematic sectors. Starting from TRL 6 software solutions, the project is developing, validating, delivering, and maturing into TRL 8 a portfolio of cutting-edge thematic services 9 for EOSC onboarding to fill current research gaps and needs, engaging user communities and identifying novel business cases and opportunities. Although there are evident differences among the NEANIAS scientific sectors, its thematic services are built upon a comprehensive collection of reusable core services, designed to tackle fundamental technical or market-oriented data life cycle tasks.…”

Section: Introductionmentioning

confidence: 99%

Scientific Visualization on the Cloud: the NEANIAS Services towards EOSC Integration

et al. 2022

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NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration.

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“…According to studies published in 2020 and 2021, archaeologists and conservators continue to use commercially tested material and composite analysis software tools to reconstruct or visualise 3D archaeological models, such as VGStudio MAX, Amira Avizo 9.0, Simpleware—Synopsys, Inc. and Dragonfly Pro—Carl Zeiss. We also recommend the possibility of online 3D visualisation of 2D images CT, with [ 89 ] interactive 2D and 3D graphics (WebGL), in any compatible web browser and without the use of plug-ins.…”

Section: Tomographic Reconstruction In Archaeologymentioning

confidence: 99%

Microtomographic Analysis of a Palaeolithic Wooden Point from the Ljubljanica River

Puhar

Korat

Erič

et al. 2022

Sensors

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A rare and valuable Palaeolithic wooden point, presumably belonging to a hunting weapon, was found in the Ljubljanica River in Slovenia in 2008. In order to prevent complete decay, the waterlogged wooden artefact had to undergo conservation treatment, which usually involves some expected deformations of structure and shape. To investigate these changes, a series of surface-based 3D models of the artefact were created before, during and after the conservation process. Unfortunately, the surface-based 3D models were not sufficient to understand the internal processes inside the wooden artefact (cracks, cavities, fractures). Since some of the surface-based 3D models were taken with a microtomographic scanner, we decided to create a volumetric 3D model from the available 2D tomographic images. In order to have complete control and greater flexibility in creating the volumetric 3D model than is the case with commercial software, we decided to implement our own algorithm. In fact, two algorithms were implemented for the construction of surface-based 3D models and for the construction of volumetric 3D models, using (1) unsegmented 2D images CT and (2) segmented 2D images CT. The results were positive in comparison with commercial software and new information was obtained about the actual state and causes of the deformation of the artefact. Such models could be a valuable aid in the selection of appropriate conservation and restoration methods and techniques in cultural heritage research.

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Modern Scientific Visualizations on the Web

Cited by 16 publications

References 138 publications

How Machine Learning is Powering Neuroimaging to Improve Brain Health

How Machine Learning is Powering Neuroimaging to Improve Brain Health

Scientific Visualization on the Cloud: the NEANIAS Services towards EOSC Integration

Microtomographic Analysis of a Palaeolithic Wooden Point from the Ljubljanica River

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