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        "id": "bayat-2024-awt",
        "type_id": "journalpaper_notalk",
        "tu_id": null,
        "repositum_id": "20.500.12708/197494",
        "title": "A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation",
        "date": "2024-01",
        "abstract": "We introduce a workflow for the visual assessment of interobserver variability in medical image segmentation. Image segmentation is a crucial step in the diagnosis, prognosis, and treatment of many diseases. Despite the advancements in autosegmentation, clinical practice widely relies on manual delineations performed by radiologists. Our work focuses on designing a solution for understanding the radiologists' thought processes during segmentation and for unveiling reasons that lead to interobserver variability. To this end, we propose a visual analysis tool connecting multiple radiologists' delineation processes with their outcomes, and we demonstrate its potential in a case study.",
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        "doi": "10.1109/MCG.2023.3333475",
        "issn": "1558-1756",
        "journal": "IEEE Computer Graphics and Applications",
        "number": "1",
        "pages": "9",
        "pages_from": "86",
        "pages_to": "94",
        "publisher": "IEEE COMPUTER SOC",
        "volume": "44",
        "research_areas": [],
        "keywords": [
            "Humans",
            "Observer Variation",
            "Workflow",
            "Algorithms"
        ],
        "weblinks": [],
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        "url": "https://www.cg.tuwien.ac.at/research/publications/2024/bayat-2024-awt/",
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    {
        "id": "bayat-2022-mva",
        "type_id": "masterthesis",
        "tu_id": null,
        "repositum_id": "20.500.12708/139147",
        "title": "Multi-faceted Visual Analysis of Inter-Observer Variability",
        "date": "2022",
        "abstract": "Despite the advancements in auto-segmentation tools, manual delineation is still necessary in the medical field. For example, tumor segmentation is a crucial step in cancer radiotherapy and is still widely performed by hand by experienced radiologists. However, the opinions of experienced radiologists might differ, for a multitude of reasons. In this work, we visualize the variability originating from multiple experts delineating medical scans of the same patient, known as inter-observer variability.The novelty of this work consists of capturing the process of segmenting a target object. The focus lies in gaining insight into the observer’s thought processes and reasoning strategies. To investigate these aspects of segmenting we conduct a data acquisitionwith novice users and experts, capturing their thoughts in a think-aloud protocol and their areas of attention by tracking their mouse-movement during the segmentation process. This data is visualized with our Multi Observer Looking Environment (MOLE).MOLE allows to gain deep insight into the observers’ segmentation process and enables to compare different segmentation outcomes and how these occurred. With our proposed visualization techniques we emphasize regions of uncertainty that need more attention when delineating. Additionally, relevant keywords are extracted from the think-aloud protocol and aligned with the positions in the segmentation, providing information about the thought process of an observer. We link the initial image to a three-dimensional representation of the delineations and provide more details of the think-aloud protocol on demand.Our approach is universal to segmentation, attention and thought process data regardless of the domain of the data. We show how MOLE can be used with a medical dataset as well as an artificially created dataset. By validating our approach with the help of a medical expert actively working in the field, we define potential use cases in the existing pipeline of tumor delineation for cancer treatment.",
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        "date_end": "2022-12",
        "date_start": "2022-03",
        "diploma_examina": "2023-01-16",
        "doi": "10.34726/hss.2022.105180",
        "matrikelnr": "01614855",
        "open_access": "yes",
        "pages": "145",
        "supervisor": [
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        "research_areas": [
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            "MedVis"
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        "keywords": [
            "medical visualization",
            "uncertainty",
            "inter-observer variability",
            "data acquisition",
            "segmentation",
            "manual delineation"
        ],
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    {
        "id": "Bayat_2019",
        "type_id": "bachelorthesis",
        "tu_id": null,
        "repositum_id": null,
        "title": "The Visualization of the Evolution of Cultural Models",
        "date": "2019-08-13",
        "abstract": "Culture is a fascinating phenomenon influencing various aspects of our lives. Cultural models seek to describe the complex structure of ethical and societal values. Two main cultural models have been defined so far: the Hofstede model [Hof11] and the GLOBE project [GLO04]. Both models define similar attributes to describe characteristics of a society, summarized in so-called cultural dimensions.\nTo better understand the complexity of cultural models and the information given\nthere are tools that visualize the complex data provided. Current tools for the visualization of cultural models make use of barcharts, boxplots and scatterplots, while only covering a small part of the data and information given. The existing tools do not cover the information completely and miss vital aspects. We want to fill these gaps and seek to find a way to easily compare selected data with each other. Moreover, we want to design a visualization that can identify cultures and cultural regions. We try to create a tool to visualize cultural models. The tool displays the given data in an easy way, by using new approaches and improving existing ones. First, we analyze the data given, to crystallize the core information and main feature of our visualization.\nNext, the goal is to define the advantages and disadvantages of the current and latest\nvisualization approaches. By combining the strengths and improving the weaknesses of\nthese existing tools we try to specify the difficulties and goals we want to achieve with the new approach. Lastly, we look at ongoing cultural applications by using the developed visualization tool and look for similarities where we do not expect them.",
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        "date_end": "2019-08-13",
        "date_start": "2019-04-10",
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        "research_areas": [
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