Hauke Bartsch


Adjunct Associate Professor

Medical Visualization, imaging, SMART on FHiR, electronic data capture (REDCap)



    [DOI] [YT] [Bibtex]
    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"
    [DOI] [Bibtex]
    title = {Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer},
    author = {Hodneland, Erlend and Kaliyugarasan, Satheshkumar and Wagner-Larsen, Kari Str{\o}no and Lura, Nj{\aa}l and Andersen, Erling and Bartsch, Hauke and Smit, Noeska and Halle, Mari Kylles{\o} and Krakstad, Camilla and Lundervold, Alexander Selvikv{\aa}g and Haldorsen, Ingfrid S},
    year = 2022,
    journal = {Cancers},
    publisher = {MDPI},
    volume = 14,
    number = 10,
    pages = 2372,
    doi = {10.3390/cancers14102372},
    url = {https://pubmed.ncbi.nlm.nih.gov/35625977/},
    images = "images/Hodneland-2022-Fully.PNG",
    thumbnails = "images/Hodneland-2022-Fully.PNG",
    project = {ttmedvis}


    [PDF] [DOI] [Bibtex]
    title = {Interactive Multimodal Imaging Visualization for Multiple Sclerosis Lesion Analysis},
    author = {Sugathan, Sherin and Bartsch, Hauke and Riemer, Frank and Gr{\"u}ner, Renate and Lawonn, Kai and Smit, Noeska N},
    year = 2021,
    booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
    publisher = {The Eurographics Association},
    doi = {10.2312/vcbm.20211346},
    isbn = {978-3-03868-140-3},
    issn = {2070-5786},
    url = {https://diglib.eg.org/handle/10.2312/vcbm20211346},
    pdf = {pdfs/Sugathan-2021-VCBM.pdf},
    thumbnails = {images/Sugathan-2021-VCBM.png},
    images = {images/Sugathan-2021-VCBM.png},
    project = {ttmedvis},
    abstract = {Multiple Sclerosis (MS) is a brain disease that is diagnosed and monitored extensively through MRI scans. One of the criteria is the appearance of so-called brain lesions. The lesions show up on MRI scans as regions with elevated or reduced contrast compared to the surrounding healthy tissue.
    Understanding the complex interplay of contrast, location and shape in images from multiple modalities from 2D MRI slices is challenging.
    Advanced visualization of appearance- and location-related features of lesions would help researchers in defining better disease characterization through MS research.
    Since a permanent cure is not possible in MS and medication-based disease modification is a common treatment path, providing better visualizations would strengthen research which investigates the effect of white matter lesions. Here we present an advanced visualization solution that supports analysis from multiple imaging modalities acquired in a clinical routine examination. The solution holds potential for enabling researchers to have a more intuitive perception of lesion features. As an example for enhancing the analytic possibilities, we demonstrate the benefits of lesion projection using both Diffusion Tensor Imaging (DTI) and gradient-based techniques. This approach enables users to assess brain structures across individuals as the atlas-based analysis provides 3D anchoring and labeling of regions across a series of brain scans from the same participant and across different participants. The projections on the brain surface also enable researchers to conduct detailed studies on the relationship between cognitive disabilities and location of lesions. This allows researchers to correlate lesions to Brodmann areas and related brain functions.
    We realize the solutions in a prototype application that supports both DTI and structural data. A qualitative evaluation demonstrates that our approach supports MS researchers by providing new opportunities for MS research.}


    [PDF] [DOI] [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}