I’m a PhD student working in the Visual Data Science for Large Scale Hypothesis Management in Imaging Biomarker Discovery (VIDI) project. This is a joint project between UiB and the Mohn Medical Imaging and Visualization (MMIV) centre, and is funded by UiB and the Trond Mohn Foundation.
My research focuses on visual analysis, exploration, and communication of physiology, drawing from my background as a medical illustrator. To learn more please check out my work below, visit laura-garrison.com, or drop me an email. I’d love to chat!
Publications
2022
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[Bibtex] @ARTICLE {Garrison2022PhysioSTAR,
author = "Laura A. Garrison and Ivan Kolesar and Ivan Viola and Helwig Hauser and Stefan Bruckner",
title = "Trends & Opportunities in Visualization for Physiology: A Multiscale Overview",
journal = "Computer Graphics Forum",
year = "2022",
abstract = "Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state-of-the-art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high-level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area. ",
images = "images/garrison-STAR-taxonomy.png",
thumbnails = "images/garrison-STAR-thumb.png",
pdf = "pdfs/Garrison_STAR_cameraready.pdf",
publisher = "The Eurographics Association and John Wiley \& Sons Ltd.",
project = "VIDI"
}2021
![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
![[VID]](https://vis.uib.no/wp-content/papercite-data/images/video.png)
[Bibtex] @Article{Kristiansen-2021-SSG,
author = {Kristiansen, Y. S. and Garrison, L. and Bruckner, S.},
title = {Semantic Snapping for Guided Multi-View Visualization Design},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
volume = {},
pages = {},
doi = {},
abstract = {Visual information displays are typically composed of multiple visualizations that are used to facilitate an understanding of the underlying data. A common example are dashboards, which are frequently used in domains such as finance, process monitoring and business intelligence. However, users may not be aware of existing guidelines and lack expert design knowledge when composing such multi-view visualizations. In this paper, we present semantic snapping, an approach to help non-expert users design effective multi-view visualizations from sets of pre-existing views. When a particular view is placed on a canvas, it is “aligned” with the remaining views–not with respect to its geometric layout, but based on aspects of the visual encoding itself, such as how data dimensions are mapped to channels. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs, as well as to provide suggestions for alternative presentations. With this approach, users can be guided to avoid common pitfalls encountered when composing visualizations. Our provided examples and case studies demonstrate the usefulness and validity of our approach.},
note = {Accepted for publication, to be presented at IEEE VIS 2021},
project = {MetaVis,VIDI},
pdf = {pdfs/Kristiansen-2021-SSG.pdf},
vid = {vids/Kristiansen-2021-SSG.mp4},
thumbnails = {images/Kristiansen-2021-SSG.png},
images = {images/Kristiansen-2021-SSG.jpg},
keywords = {tabular data, guidelines, mixed initiative human-machine analysis, coordinated and multiple views},
doi = {10.1109/TVCG.2021.3114860},
}@InProceedings{Garrison-2021-EPP,
author = {Laura Garrison and Monique Meuschke and Jennifer Fairman and Noeska Smit and Bernhard Preim and Stefan Bruckner},
title = {An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes},
booktitle = {Proceedings of VCBM},
year = {2021},
pages = {},
doi = {},
abstract = {The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design.},
note = {Accepted for publication, to be presented at VCBM 2021},
project = {VIDI,ttmedvis},
pdf = {pdfs/Garrison-2021-EPP.pdf},
thumbnails = {images/Garrison-2021-EPP.png},
images = {images/Garrison-2021-EPP.jpg},
url = {https://github.com/lauragarrison87/Biomedical_Process_Vis},
keywords = {biomedical illustration, visual communication, survey},
}![[YT]](https://vis.uib.no/wp-content/papercite-data/images/youtube.png)
[Bibtex] @ARTICLE {Garrison-2021-DimLift,
author = {Garrison, Laura and M\"{u}ller, Juliane and Schreiber, Stefanie and Oeltze-Jafra, Steffen and Hauser, Helwig and Bruckner, Stefan},
title = {DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.},
volume = {27},
number = {6},
pages = {2908--2922},
pdf = {pdfs/garrison-2021-dimlift.pdf},
images = {images/garrison_dimlift.jpg},
thumbnails = {images/garrison_dimlift_thumb.jpg},
youtube = {https://youtu.be/JSZuhnDyugA},
doi = {10.1109/TVCG.2021.3057519},
git = {https://github.com/lauragarrison87/DimLift},
project = {VIDI},
}@ARTICLE {Mueller-2021-IDA,
author = {M\"{u}ller, Juliane and Garrison, Laura and Ulbrich, Philipp and Schreiber, Stefanie and Bruckner, Stefan and Hauser, Helwig and Oeltze-Jafra, Steffen},
title = {Integrated Dual Analysis of Quantitative and Qualitative High-Dimensional Data},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The Dual Analysis framework is a powerful enabling technology for the exploration of high dimensional quantitative data by treating data dimensions as first-class objects that can be explored in tandem with data values. In this work, we extend the Dual Analysis framework through the joint treatment of quantitative (numerical) and qualitative (categorical) dimensions. Computing common measures for all dimensions allows us to visualize both quantitative and qualitative dimensions in the same view. This enables a natural joint treatment of mixed data during interactive visual exploration and analysis. Several measures of variation for nominal qualitative data can also be applied to ordinal qualitative and quantitative data. For example, instead of measuring variability from a mean or median, other measures assess inter-data variation or average variation from a mode. In this work, we demonstrate how these measures can be integrated into the Dual Analysis framework to explore and generate hypotheses about high-dimensional mixed data. A medical case study using clinical routine data of patients suffering from Cerebral Small Vessel Disease (CSVD), conducted with a senior neurologist and a medical student, shows that a joint Dual Analysis approach for quantitative and qualitative data can rapidly lead to new insights based on which new hypotheses may be generated.},
volume = {27},
number = {6},
pages = {2953--2966},
pdf = {pdfs/Mueller_2020_IDA.pdf},
images = {images/Mueller_2020_IDA.jpg},
thumbnails = {images/Mueller_2020_IDA.png},
doi = {10.1109/TVCG.2021.3056424},
git = {https://github.com/JulianeMu/IntegratedDualAnalysisAproach_MDA},
project = {VIDI},
}2020
[Bibtex] @article{Garrison-2020-IVE,
author = {Garrison, Laura and Va\v{s}\'{i}\v{c}ek, Jakub and Craven, Alex R. and Gr\"{u}ner, Renate and Smit, Noeska and Bruckner, Stefan},
title = {Interactive Visual Exploration of Metabolite Ratios in MR Spectroscopy Studies},
journal = {Computers \& Graphics},
volume = {92},
pages = {1--12},
keywords = {medical visualization, magnetic resonance spectroscopy data, information visualization, user-centered design},
doi = {10.1016/j.cag.2020.08.001},
abstract = {Magnetic resonance spectroscopy (MRS) is an advanced biochemical technique used to identify metabolic compounds in living tissue. While its sensitivity and specificity to chemical imbalances render it a valuable tool in clinical assessment, the results from this modality are abstract and difficult to interpret. With this design study we characterized and explored the tasks and requirements for evaluating these data from the perspective of a MRS research specialist. Our resulting tool, SpectraMosaic, links with upstream spectroscopy quantification software to provide a means for precise interactive visual analysis of metabolites with both single- and multi-peak spectral signatures. Using a layered visual approach, SpectraMosaic allows researchers to analyze any permutation of metabolites in ratio form for an entire cohort, or by sample region, individual, acquisition date, or brain activity status at the time of acquisition. A case study with three MRS researchers demonstrates the utility of our approach in rapid and iterative spectral data analysis.},
year = {2020},
pdf = "pdfs/Garrison-2020-IVE.pdf",
thumbnails = "images/Garrison-2020-IVE.png",
images = "images/Garrison-2020-IVE.jpg",
project = "VIDI",
git = "https://github.com/mmiv-center/spectramosaic-public",
}2019
[Bibtex] @inproceedings {Bartsch-2019-MVA,
booktitle = {Proceedings of VCBM 2019 (Short Papers)},
title = {MedUse: A Visual Analysis Tool for Medication Use Data in the ABCD Study},
author = {Bartsch, Hauke and Garrison, Laura and Bruckner, Stefan and Wang, Ariel and Tapert, Susan F. and Gr\"{u}ner, Renate},
abstract = {The RxNorm vocabulary is a yearly-published biomedical resource providing normalized names for medications. It is used to capture medication use in the Adolescent Brain Cognitive Development (ABCD) study, an active and publicly available longitudinal research study following 11,800 children over 10 years. In this work, we present medUse, a visual tool allowing researchers to explore and analyze the relationship of drug category to cognitive or imaging derived measures using ABCD study data. Our tool provides position-based context for tree traversal and selection granularity of both study participants and drug category. Developed as part of the Data Exploration and Analysis Portal (DEAP), medUse is available to more than 600 ABCD researchers world-wide. By integrating medUse into an actively used research product we are able to reach a wide audience and increase the practical relevance of visualization for the biomedical field.},
year = {2019},
pages = {97--101},
images = "images/Bartsch-2019-MVA.jpg",
thumbnails = "images/Bartsch-2019-MVA.png",
pdf = "pdfs/Bartsch-2019-MVA.pdf",
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-081-9},
DOI = {10.2312/vcbm.20191236},
project = {VIDI}
}@INPROCEEDINGS {Garrison2019SM,
author = {Garrison, Laura and Va\v{s}\'{\i}\v{c}ek, Jakub and Gr\"{u}ner, Renate and Smit, Noeska and Bruckner, Stefan},
title = {SpectraMosaic: An Exploratory Tool for the Interactive Visual Analysis of Magnetic Resonance Spectroscopy Data},
journal = {Computer Graphics Forum},
month = {sep},
year = {2019},
booktitle = {Proceedings of VCBM 2019},
pages = {1--10},
event = "VCBM 2019",
proceedings = "Proceedings of the 9th Eurographics Workshop on Visual Computing in Biology and Medicine",
keywords = {medical visualization, magnetic resonance spectroscopy data, information visualization, user-centered design},
images = "images/garrison_VCBM19spectramosaic_full.PNG",
thumbnails = "images/garrison_VCBM19spectramosaic_thumb.png",
pdf = "pdfs/garrison_VCBM19spectramosaic.pdf",
youtube = "https://www.youtube.com/watch?v=Rzl7sl4WvdQ",
abstract = {Magnetic resonance spectroscopy (MRS) allows for assessment of tissue metabolite characteristics used often for early detection and treatment evaluation of brain-related pathologies. However, meaningful variations in ratios of tissue metabolites within a sample area are difficult to capture with current visualization tools. Furthermore, the learning curve to interpretation is steep and limits the more widespread adoption of MRS in clinical practice. In this design study, we collaborated with domain experts to design a novel visualization tool for the exploration of tissue metabolite concentration ratios in spectroscopy clinical and research studies. We present a data and task analysis for this domain, where MRS data attributes can be categorized into tiers of visual priority. We furthermore introduce a novel set of visual encodings for these attributes. Our result is SpectraMosaic (see Figure~\ref{fig:teaser}), an interactive insight-generation tool for rapid exploration and comparison of metabolite ratios. We validate our approach with two case studies from MR spectroscopy experts, providing early qualitative evidence of the efficacy of the system for visualization of spectral data and affording deeper insights into these complex heterogeneous data.},
git = "https://git.app.uib.no/Laura.Garrison/spectramosaic",
doi = "0.2312/vcbm.20191225",
project = "VIDI"
}