Immersive Analytics Applications in Life and Health Sciences

Czauderna, Tobias; Haga, Jason; Kim, Jin Man; Klapperstück, Matthias; Klein, Karsten; Kuhlen, Torsten; Oeltze-Jafra, Steffen; Sommer, Björn; Schreiber, Falk

doi:10.1007/978-3-030-01388-2_10

Cited by 11 publications

(7 citation statements)

References 120 publications

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“…They can support stereoscopic 3D data visualization, motion tracking with up to six degrees of freedom, and a wide field of view, and thus broaden the design space for information representation and communication approaches. Such features can significantly enhance the capabilities of a data analysis, with huge potential, particularly for the life and health sciences [ 12 ]. However, corresponding solutions need to be carefully designed and tailored toward the use case to facilitate an effective and efficient analysis by human domain experts, avoiding issues stemming from the limitations of both the devices and human perception and cognition [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Spatially resolved transcriptomics in immersive environments

Bienroth

Nim

Garkov

et al. 2022

Vis. Comput. Ind. Biomed. Art

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Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomicsis presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

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Section: Introductionmentioning

confidence: 99%

Spatially resolved transcriptomics in immersive environments

Bienroth

Nim

Garkov

et al. 2022

Vis. Comput. Ind. Biomed. Art

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“…In SciVis‐related areas, the use of virtual environments for the visualization of spatial data has been common for decades – already, the first CAVEs in the early 90s were often dedicated to SciVis [Bry96, CNSD93]. IA employs many methods of SciVis and is used in various application areas such as archaeology [KF12, SKD*13], geosciences [HOJ*07] and the life sciences [CHK*18, SBH*14]. Also in industry, CAVEs and other AR/VR environments are steadily gaining in popularity in various sectors, such as in healthcare, aviation or automotive industry [vir21].…”

Section: Introductionmentioning

confidence: 99%

Immersive Analytics with Abstract 3D Visualizations: A Survey

Kraus

Fuchs

Sommer

et al. 2021

Computer Graphics Forum

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After a long period of scepticism, more and more publications describe basic research but also practical approaches to how abstract data can be presented in immersive environments for effective and efficient data understanding. Central aspects of this important research question in immersive analytics research are concerned with the use of 3D for visualization, the embedding in the immersive space, the combination with spatial data, suitable interaction paradigms and the evaluation of use cases. We provide a characterization that facilitates the comparison and categorization of published works and present a survey of publications that gives an overview of the state of the art, current trends, and gaps and challenges in current research.

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“…A-BI + can be applied to different application domains ranging from healthcare [12] to factories [13,14]; for this reason, the main modeling choices under-lying our approach (e.g., how to define the relevance of an object in a context) have been formalized in a domain-independent fashion, while domain-dependent examples are provided in the context of AR in a factory where fitness equipment is produced.…”

Section: Introductionmentioning

confidence: 99%