I’m an Associate Professor in Medical Visualization (tenure track position funded by the Trond Mohn Foundation), with a background in computer science and as a radiographer. I am also affiliated to the Mohn Medical Imaging and Visualization (MMIV) centre as a senior researcher and part of the leadership team. Currently, I am researching novel interactive visualization approaches for improved exploration, analysis, and communication of multimodal medical imaging data. The focus of our team in this context is on multi-parametric MR acquisitions.
Teaching:
- Continuously INF219, INF319 Project in Visualization
- Fall semester 2020: INF219 Project in Visualization: new style involving real clients and seminar lectures
- Spring Semester 2019: INF101 Object-oriented Programming
- Fall Semester 2017/2018: INF358 Seminar in Visualization
This page only displays publications I have authored in my current affiliation. For a full overview, please check my Google Scholar profile. For more information, please check my personal website.
Publications
2021
![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
[Bibtex] @incollection{Smit-2021-COMULIS,
author = {Smit, Noeska and Bühler, Katja and Vilanova, Anna and Falk, Martin},
title = {Visualisation for correlative multimodal imaging},
booktitle = {Imaging Modalities for Biological and Preclinical Research: A Compendium, Volume 2},
publisher = {IOP Publishing},
year = {2021},
series = {2053-2563},
type = {Book Chapter},
pages = {III.4.e-1 to III.4.e-10},
abstract = {In this chapter, we describe several approaches to interactive imaging data visualization in general, highlight several strategies for visualizing correlative multimodal imaging data, and provide examples and practical recommendations.},
url = {http://dx.doi.org/10.1088/978-0-7503-3747-2ch28},
doi = {10.1088/978-0-7503-3747-2ch28},
isbn = {978-0-7503-3747-2},
thumbnails = "images/Smit-2021-COMULIS.PNG",
images = "images/Smit-2021-COMULIS.PNG",
project = "ttmedvis",
abstract = {The field of visualisation deals with finding appropriate visual representations of data so people can effectively carry out tasks related to data exploration, analysis, or presentation using the power of the human visual perceptual system. In the context of biomedical imaging data, interactive visualisation techniques can be employed, for example, to visually explore data, as image processing quality assurance, or in publications to communicate findings. When dealing with correlative imaging, challenges arise in how to effectively convey the information from multiple sources. In particular, the information density leads to the need for a critical reflection on the visual design with respect to which parts of the data are important to show and at what level of importance they should be visualised. In this chapter, we describe several approaches to interactive imaging data visualisation in general, highlight several strategies for visualising correlative multimodal imaging data, and provide examples and practical recommendations.}
}2020
![[PDF]](https://vis.uib.no/wp-content/plugins/papercite/img/pdf.png)
[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",
}@article{RadEx,
author = {M\"{o}rth, E. and Wagner-Larsen, K. and Hodneland, E. and Krakstad, C. and Haldorsen, I. S. and Bruckner, S. and Smit, N. N.},
title = {RadEx: Integrated Visual Exploration of Multiparametric Studies for Radiomic Tumor Profiling},
journal = {Computer Graphics Forum},
volume = {39},
number = {7},
year = {2020},
pages = {611--622},
abstract = {Better understanding of the complex processes driving tumor growth and metastases is critical for developing targeted treatment strategies in cancer. Radiomics extracts large amounts of features from medical images which enables radiomic tumor profiling in combination with clinical markers. However, analyzing complex imaging data in combination with clinical data is not trivial and supporting tools aiding in these exploratory analyses are presently missing. In this paper, we present an approach that aims to enable the analysis of multiparametric medical imaging data in combination with numerical, ordinal, and categorical clinical parameters to validate established and unravel novel biomarkers. We propose a hybrid approach where dimensionality reduction to a single axis is combined with multiple linked views allowing clinical experts to formulate hypotheses based on all available imaging data and clinical parameters. This may help to reveal novel tumor characteristics in relation to molecular targets for treatment, thus providing better tools for enabling more personalized targeted treatment strategies. To confirm the utility of our approach, we closely collaborate with experts from the field of gynecological cancer imaging and conducted an evaluation with six experts in this field.},
pdf = "pdfs/Moerth-2020-RadEx.pdf",
images = "images/Moerth-2020-RadEx.jpg",
thumbnails = "images/Moerth-2020-RadEx-thumb.jpg",
project = "ttmedvis",
doi = {10.1111/cgf.14172}
}![[YT]](https://vis.uib.no/wp-content/papercite-data/images/youtube.png)
[Bibtex] @INPROCEEDINGS{Moerth-2020-CGI,
author = "M\"{o}rth, E. and Haldorsen, I.S. and Bruckner, S. and Smit, N.N.",
title = "ParaGlyder: Probe-driven Interactive Visual Analysis for Multiparametric Medical Imaging Data",
booktitle = "Proceedings of Computer Graphics International",
pages = "351--363",
year = "2020",
abstract = "Multiparametric medical imaging describes approaches that include multiple imaging sequences acquired within the same imaging examination, as opposed to one single imaging sequence or imaging from multiple imaging modalities. Multiparametric imaging in cancer has been shown to be useful for tumor detection and may also depict functional tumor characteristics relevant for clinical phenotypes. However, when confronted with datasets consisting of multiple values per voxel, traditional reading of the imaging series fails to capture complicated patterns. Those patterns of potentially important imaging properties of the parameter space may be critical for the analysis. Standard approaches, such as transfer functions and juxtapositioned visualizations, fail to convey the shape of the multiparametric parameter distribution in sufficient detail. For these reasons, in this paper we present an approach that aims to enable the exploration and analysis of such multiparametric studies using an interactive visual analysis application to remedy the trade-offs between details in the value domain and in spatial resolution. Interactive probing within or across subjects allows for a digital biopsy that is able to uncover multiparametric tissue properties. This may aid in the discrimination between healthy and cancerous tissue, unravel radiomic tissue features that could be linked to targetable pathogenic mechanisms, and potentially highlight metastases that evolved from the primary tumor. We conducted an evaluation with eleven domain experts from the field of gynecological cancer imaging, neurological imaging, and machine learning research to confirm the utility of our approach.",
note= "The final authenticated version is available online at https://doi.org/10.1007/978-3-030-61864-3_29",
pdf = "pdfs/Moerth-2020-CGI-ParaGlyder.pdf",
images = "images/Moerth-2020-ParaGlyder.PNG",
thumbnails = "images/Moerth-2020-ParaGlyder-thumb.png",
youtube = "https://youtu.be/S_M4CWXKz0U",
publisher = "LNCS by Springer",
project = "ttmedvis",
doi = "10.1007/978-3-030-61864-3_29"
}@article{Solteszova-2019-MLT,
author = {Solteszova, V. and Smit, N. N. and Stoppel, S. and Grüner, R. and Bruckner, S.},
title = {Memento: Localized Time-Warping for Spatio-Temporal Selection},
journal = {Computer Graphics Forum},
volume = {39},
number = {1},
pages = {231--243},
year = {2020},
keywords = {interaction, temporal data, visualization, spatio-temporal projection},
images = "images/Solteszova-2019-MLT.jpg",
thumbnails = "images/Solteszova-2019-MLT-1.jpg",
pdf = "pdfs/Solteszova-2019-MLT.pdf",
doi = {10.1111/cgf.13763},
abstract = {Abstract Interaction techniques for temporal data are often focused on affecting the spatial aspects of the data, for instance through the use of transfer functions, camera navigation or clipping planes. However, the temporal aspect of the data interaction is often neglected. The temporal component is either visualized as individual time steps, an animation or a static summary over the temporal domain. When dealing with streaming data, these techniques are unable to cope with the task of re-viewing an interesting local spatio-temporal event, while continuing to observe the rest of the feed. We propose a novel technique that allows users to interactively specify areas of interest in the spatio-temporal domain. By employing a time-warp function, we are able to slow down time, freeze time or even travel back in time, around spatio-temporal events of interest. The combination of such a (pre-defined) time-warp function and brushing directly in the data to select regions of interest allows for a detailed review of temporally and spatially localized events, while maintaining an overview of the global spatio-temporal data. We demonstrate the utility of our technique with several usage scenarios.},
project = "MetaVis,ttmedvis,VIDI"
}2019
[Bibtex] @article{kraima2019role,
title={The role of the longitudinal muscle in the anal sphincter complex: Implications for the Intersphincteric Plane in Low Rectal Cancer Surgery?},
author={Kraima, Anne C and West, Nicholas P and Roberts, Nicholas and Magee, Derek R and Smit, Noeska N and van de Velde, Cornelis JH and DeRuiter, Marco C and Rutten, Harm J and Quirke, Philip},
journal={Clinical Anatomy},
year={2019},
doi="10.1002/ca.23444",
url = "https://onlinelibrary.wiley.com/doi/full/10.1002/ca.23444",
publisher={Wiley Online Library},
project = "ttmedvis",
images = {images/kraima-2019-role.png},
thumbnails = {images/kraima-2019-role.png},
abstract = {Intersphincteric resection (ISR) enables radical sphincter-preserving surgery in a subset of low rectal tumors impinging on the anal sphincter complex (ASC). Excellent anatomical knowledge is essential for optimal ISR. This study describes the role of the longitudinal muscle (LM) in the ASC and implications for ISR and other low rectal and anal pathologies. Six human adult en bloc cadaveric specimens (three males, three females) were obtained from the University of Leeds GIFT Research Tissue Programme. Paraffin-embedded mega blocks containing the ASC were produced and serially sectioned at 250?µm intervals. Whole mount microscopic sections were histologically stained and digitally scanned. The intersphincteric plane was shown to be potentially very variable. In some places adipose tissue is located between the external anal sphincter (EAS) and internal anal sphincter (IAS), whereas in others the LM interdigitates to obliterate the plane. Elsewhere the LM is (partly) absent with the intersphincteric plane lying on the IAS. The LM gave rise to the formation of the submucosae and corrugator ani muscles by penetrating the IAS and EAS. In four of six specimens, striated muscle fibers from the EAS curled around the distal IAS reaching the anal submucosa. The ASC formed a complex structure, varying between individuals with an inconstant LM affecting the potential location of the intersphincteric plane as well as a high degree of intermingling striated and smooth muscle fibers potentially further disrupting the plane. The complexity of identifying the correct pathological staging of low rectal cancer is also demonstrated.}
}@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"
}@incollection{Smit-2019-AtlasVis,
title={Towards Advanced Interactive Visualization for Virtual Atlases},
author={Smit, Noeska and Bruckner, Stefan},
booktitle={Biomedical Visualisation},
pages={85--96},
year={2019},
publisher={Springer},
doi = {10.1007/978-3-030-19385-0_6},
url = "http://noeskasmit.com/wp-content/uploads/2019/07/Smit_AtlasVis_2019.pdf",
images = "images/Smit-2019-AtlasVis.png",
thumbnails = "images/Smit-2019-AtlasVis.png",
abstract = "An atlas is generally defined as a bound collection of tables, charts or illustrations describing a phenomenon. In an anatomical atlas for example, a collection of representative illustrations and text describes anatomy for the purpose of communicating anatomical knowledge. The atlas serves as reference frame for comparing and integrating data from different sources by spatially or semantically relating collections of drawings, imaging data, and/or text. In the field of medical image processing, atlas information is often constructed from a collection of regions of interest, which are based on medical images that are annotated by domain experts. Such an atlas may be employed for example for automatic segmentation of medical imaging data. The combination of interactive visualization techniques with atlas information opens up new possibilities for content creation, curation, and navigation in virtual atlases. With interactive visualization of atlas information, students are able to inspect and explore anatomical atlases in ways that were not possible with the traditional method of presenting anatomical atlases in book format, such as viewing the illustrations from other viewpoints. With advanced interaction techniques, it becomes possible to query the data that forms the basis for the atlas, thus empowering researchers to access a wealth of information in new ways. So far, atlasbased visualization has been employed for mainly medical education, as well as biological research. In this survey, we provide an overview of current digital biomedical atlas tasks and applications and summarize relevant visualization techniques. We discuss recent approaches for providing next-generation visual interfaces to navigate atlas data that go beyond common text-based search and hierarchical lists. Finally, we reflect on open challenges and opportunities for the next steps in interactive atlas visualization. ",
project = "ttmedvis,MetaVis,VIDI"
}