Eric Mörth

PhD Student

 Team Smit

Publications

2020

    [PDF] [DOI] [Bibtex]
    @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}
    }
    [PDF] [DOI] [YT] [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"
    }