Contact: Noeska Smit
Members:
Noeska Smit
This grant substantiates a tenure track position supported by Bergen Research Foundation and the University of Bergen. Noeska Smit from the Netherlands started in her position as associate professor at the Department of Informatics, UiB, January 1, 2017. Smit’s specialty is in medical visualization. The recruitment of Smit is part of a joint priority of the field of medical visualization at Helse Bergen and University of Bergen. The common priority also has resulted in the establishment of a joint center for medical imaging and visualization located at Helse Bergen This center is also supported economically by Bergen Research Foundation.
The visualization group at Department of Informatics has a leading position in the visualization of complex data from numerous application domains and is actively engaged in research in the emerging field of computational medicine. The recruitment of Smit is a strategic move towards this field and represents a strengthening of the fundamental basic research which will be of such crucial importance for the newly established medical imaging and visualization centre: MMIV.
Related links:
Publications
2022
![[PDF]](https://vis.uib.no/wp-content/plugins/papercite/img/pdf.png)
[Bibtex] @phdthesis{moerth2022thesis,
title = {Scaling Up Medical Visualization: Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication},
author = {Mörth, Eric},
year = 2022,
month = {September},
isbn = 9788230862193,
url = {https://hdl.handle.net/11250/3014336},
school = {Department of Informatics, University of Bergen, Norway},
abstract = {
Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data.
This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition.
Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.
},
pdf = {pdfs/Moerth-PhD-Thesis-2022.pdf},
images = {images/Moerth-PhD-Thesis-2022.PNG},
thumbnails = {images/Moerth-PhD-Thesis-2022.PNG},
project = {ttmedvis}
}![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
[Bibtex] @inproceedings {EichnerMoerth2022MuSIC,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Renata G. Raidou and Björn Sommer and Torsten W. Kuhlen and Michael Krone and Thomas Schultz and Hsiang-Yun Wu},
title = {{MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks}},
author = {Eichner, Tanja* and Mörth, Eric* and Wagner-Larsen, Kari S. and Lura, Njål and Haldorsen, Ingfrid S. and Gröller, Eduard and Bruckner, Stefan and Smit, Noeska N.},
note = {Best Paper Honorable Mention at VCBM2022},
project = {ttmedvis},
year = {2022},
abstract = {In gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-defined segmentation mask generated for one of the sequences. Our contributions lie in our proposed workflow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future.
Best Paper Honorable Mention at VCBM2022},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-177-9},
DOI = {10.2312/vcbm.20221190},
pdf = {pdfs/EichnerMoerth_2022.pdf},
thumbnails = {images/EichnerMoerth_2022.PNG},
images = {images/EichnerMoerth_2022.PNG},
}@inproceedings {Kleinau2022Tornado,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Renata G. Raidou and Björn Sommer and Torsten W. Kuhlen and Michael Krone and Thomas Schultz and Hsiang-Yun Wu},
title = {{Is there a Tornado in Alex's Blood Flow? A Case Study for Narrative Medical Visualization}},
project = {ttmedvis},
author = {Kleinau, Anna and Stupak, Evgenia and Mörth, Eric and Garrison, Laura A. and Mittenentzwei, Sarah and Smit, Noeska N. and Lawonn, Kai and Bruckner, Stefan and Gutberlet, Matthias and Preim, Bernhard and Meuschke, Monique},
year = {2022},
abstract = {Narrative visualization advantageously combines storytelling with new media formats and techniques, like interactivity, to create improved learning experiences. In medicine, it has the potential to improve patient understanding of diagnostic procedures and treatment options, promote confidence, reduce anxiety, and support informed decision-making. However, limited scientific research has been conducted regarding the use of narrative visualization in medicine. To explore the value of narrative visualization in this domain, we introduce a data-driven story to inform a broad audience about the usage of measured blood flow data to diagnose and treat cardiovascular diseases. The focus of the story is on blood flow vortices in the aorta, with which imaging technique they are examined, and why they can be dangerous. In an interdisciplinary team, we define the main contents of the story and the resulting design questions. We sketch the iterative design process and implement the story based on two genres. In a between-subject study, we evaluate the suitability and understandability of the story and the influence of different navigation concepts on user experience. Finally, we discuss reusable concepts for further narrative medical visualization projects.},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-177-9},
DOI = {10.2312/vcbm.20221183},
pdf = {pdfs/Kleinau_2022.pdf},
thumbnails = {images/Kleinau_2022.PNG},
images = {images/Kleinau_2022.PNG},
}@Article{Moerth2022ScrollyVis,
author = {Mörth, Eric and Bruckner, Stefan and Smit, Noeska N.},
title = {ScrollyVis: Interactive visual authoring of guided dynamic narratives for scientific scrollytelling},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2022},
volume = {},
abstract = {Visual stories are an effective and powerful tool to convey specific information to a diverse public. Scrollytelling is a recent visual storytelling technique extensively used on the web, where content appears or changes as users scroll up or down a page. By employing the familiar gesture of scrolling as its primary interaction mechanism, it provides users with a sense of control, exploration and discoverability while still offering a simple and intuitive interface. In this paper, we present a novel approach for authoring, editing, and presenting data-driven scientific narratives using scrollytelling. Our method flexibly integrates common sources such as images, text, and video, but also supports more specialized visualization techniques such as interactive maps as well as scalar field and mesh data visualizations. We show that scrolling navigation can be used to traverse dynamic narratives and demonstrate how it can be combined with interactive parameter exploration. The resulting system consists of an extensible web-based authoring tool capable of exporting stand-alone stories that can be hosted on any web server. We demonstrate the power and utility of our approach with case studies from several diverse scientific fields and with a user study including 12 participants of diverse professional backgrounds. Furthermore, an expert in creating interactive articles assessed the usefulness of our approach and the quality of the created stories.},
project = {ttmedvis},
pdf = {pdfs/Moerth_2022_ScrollyVis.pdf},
thumbnails = {images/Moerth_2022_ScrollyVis.png},
images = {images/Moerth_2022_ScrollyVis.png},
pages={1-12},
doi={10.1109/TVCG.2022.3205769},
}![[VID]](https://vis.uib.no/wp-content/papercite-data/images/video.png)
[Bibtex] @article{Moerth2022ICEVis,
title = {ICEVis: Interactive Clustering Exploration for tumor sub-region analysis in multiparametric cancer imaging},
author = {Mörth, Eric and Eichner, Tanja and Ingfrid, Haldorsen and Bruckner, Stefan and Smit, Noeska N.},
year = 2022,
journal = {Proceedings of the International Symposium on Visual Information Communication and Interaction (VINCI'22)},
volume = {15},
pages = {5},
doi = {10.1145/3554944.3554958},
issn = {},
url = {},
abstract = {Tumor tissue characteristics derived from imaging data are gaining importance in clinical research. Tumor sub-regions may play a critical role in defining tumor types and may hold essential information about tumor aggressiveness. Depending on the tumor’s location within the body, such sub-regions can be easily identified and determined by physiology, but these sub-regions are not readily visible to others. Regions within a tumor are currently explored by comparing the image sequences and analyzing the tissue heterogeneity present. To improve the exploration of such tumor sub-regions, we propose a visual analytics tool called ICEVis. ICEVis supports the identification of tumor sub-regions and corresponding features combined with cluster visualizations highlighting cluster validity. It is often difficult to estimate the optimal number of clusters; we provide rich facilities to support this task, incorporating various statistical measures and interactive exploration of the results. We evaluated our tool with three clinical researchers to show the potential of our approach.
Best Short Paper at VINCI2022},
images = "images/Moerth_2022_ICEVis.png",
thumbnails = "images/Moerth_2022_ICEVis.png",
pdf = {pdfs/Moerth_2022_ICEVis.pdf},
vid = {vids/ICEVis.mp4},
project = "ttmedvis",
}![[YT]](https://vis.uib.no/wp-content/papercite-data/images/youtube.png)
[Bibtex] @article{Sugathan2022Longitudinal,
title = {Longitudinal visualization for exploratory analysis of multiple sclerosis lesions},
author = {Sugathan, Sherin and Bartsch, Hauke and Riemer, Frank and Gr{\"u}ner, Renate and Lawonn, Kai and Smit, Noeska},
year = 2022,
journal = {Computers & Graphics},
volume = 107,
pages = {208--219},
doi = {10.1016/j.cag.2022.07.023},
issn = {0097-8493},
url = {https://www.sciencedirect.com/science/article/pii/S0097849322001479},
images = "images/Sugathan-2022-Longitudinal.PNG",
thumbnails = "images/Sugathan-2022-Longitudinal.PNG",
project = {ttmedvis},
youtube = "https://youtu.be/uwcqSf1W-dc"
}@article{VandenBossche2022Digital,
title = {Digital body preservation: Technique and applications},
author = {Vandenbossche, Vicky and Van de Velde, Joris and Avet, Stind and Willaert, Wouter and Soltvedt, Stian and Smit, Noeska and Audenaert, Emmanuel},
year = 2022,
journal = {Anatomical Sciences Education},
volume = {15},
number = {4},
pages = {731--744},
doi = {https://doi.org/10.1002/ase.2199},
url = {https://anatomypubs.onlinelibrary.wiley.com/doi/abs/10.1002/ase.2199},
images = "images/VandenBossche-2022-Digital.PNG",
thumbnails = "images/VandenBossche-2022-Digital.PNG",
project = {ttmedvis}
}@article{Wagner2022Interobserver,
title = {Interobserver agreement and prognostic impact for {MRI}--based 2018 {FIGO} staging parameters in uterine cervical cancer},
author = {Wagner-Larsen, Kari S and Lura, Nj{\aa}l and Salvesen, {\O}yvind and Halle, Mari Kylles{\o} and Forsse, David and Trovik, Jone and Smit, Noeska and Krakstad, Camilla and Haldorsen, Ingfrid S},
year = 2022,
journal = {European Radiology},
publisher = {Springer},
pages = {1--12},
doi = {10.1007/s00330-022-08666-x},
url = {https://link.springer.com/article/10.1007/s00330-022-08666-x},
images = "images/Wagner-2022-Interobserver.PNG",
thumbnails = "images/Wagner-2022-Interobserver.PNG",
project = {ttmedvis}
}