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A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation.

Hannah Clara Bayat, Manuela Waldner, Renata G Raidou

    IEEE Computer Graphics and Applications
    |January 25, 2024
    PubMed
    Summary
    This summary is machine-generated.

    We developed a visual tool to understand how radiologists segment medical images, helping to identify reasons for differences in their work. This aids in improving diagnostic accuracy and treatment planning.

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    Area of Science:

    • Radiology and Medical Imaging
    • Health Informatics
    • Data Visualization

    Background:

    • Medical image segmentation is vital for disease diagnosis, prognosis, and treatment.
    • Manual segmentation by radiologists remains standard despite automated methods.
    • Understanding interobserver variability is key to improving segmentation consistency.

    Purpose of the Study:

    • To introduce a workflow for visually assessing interobserver variability in medical image segmentation.
    • To explore the thought processes of radiologists during segmentation.
    • To uncover the underlying reasons for discrepancies in manual delineations.

    Main Methods:

    • Development of a visual analysis tool.
    • Connecting radiologists' delineation processes with segmentation outcomes.
    • Case study demonstration of the tool's potential.

    Main Results:

    • The proposed tool facilitates the visual assessment of segmentation variability.
    • The case study illustrated the tool's utility in identifying sources of interobserver differences.
    • The workflow aids in understanding radiologists' decision-making in segmentation tasks.

    Conclusions:

    • The visual assessment workflow offers a novel approach to analyzing interobserver variability in medical image segmentation.
    • This tool can help standardize segmentation practices and improve diagnostic reliability.
    • Further application of this tool can lead to more consistent and accurate medical image analysis.