A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s): not specified
• Date: January 2024
• DOI: 10.1109/MCG.2023.3333475
• ISSN: 1558-1756
• Journal: IEEE Computer Graphics and Applications
• Number: 1
• Pages: 9
• Volume: 44
• Publisher: IEEE COMPUTER SOC
• Pages: 86 – 94
• Keywords: Humans, Observer Variation, Workflow, Algorithms

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.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1109/MCG.2023.3333475

BibTeX

@article{bayat-2024-awt,
  title =      "A Workflow to Visually Assess Interobserver Variability in
               Medical Image Segmentation",
  author =     "Hannah Bayat and Manuela Waldner and Renata Raidou",
  year =       "2024",
  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.",
  month =      jan,
  doi =        "10.1109/MCG.2023.3333475",
  issn =       "1558-1756",
  journal =    "IEEE Computer Graphics and Applications",
  number =     "1",
  pages =      "9",
  volume =     "44",
  publisher =  "IEEE COMPUTER SOC",
  pages =      "86--94",
  keywords =   "Humans, Observer Variation, Workflow, Algorithms",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/bayat-2024-awt/",
}