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User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.

Anjana Ramkumar1, Jose Dolz2, Hortense A Kirisli2

  • 1Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE, Delft, The Netherlands.

Journal of Digital Imaging
|November 11, 2015
PubMed
Summary
This summary is machine-generated.

This study evaluated semi-automatic segmentation methods for radiotherapy planning. Findings suggest improving user interactions in these methods to reduce cognitive load and enhance flexibility for physicians.

Keywords:
CorrelationsEvaluationHuman-computer interactionOrgans at riskRadiotherapySemi-automatic segmentation

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Area of Science:

  • Medical Imaging
  • Radiotherapy Planning
  • Human-Computer Interaction

Background:

  • Accurate organ segmentation is crucial for radiotherapy planning.
  • Manual segmentation is time-consuming and variable.
  • Automated methods often require post-processing corrections.

Purpose of the Study:

  • To evaluate two semi-automatic segmentation methods ('strokes' and 'contour') based on user interaction.
  • To analyze the impact of human-computer interaction on segmentation quality and process.
  • To provide insights for improving semi-automatic segmentation design.

Main Methods:

  • Two physicians performed segmentation on 42 cases across five organs at risk.
  • Evaluated subjective and objective measures of interaction process and segmentation quality.
  • Correlated various process and result measures for both 'strokes' and 'contour' methods.

Main Results:

  • Identified 36 quantifiable and 10 non-quantifiable correlations per interaction type.
  • Found strong or moderate correlations in 20 measures for 'contour' and 22 for 'strokes'.
  • Demonstrated significant relationships between interaction design and segmentation outcomes.

Conclusions:

  • Semi-automatic segmentation requires less cognitively demanding user interactions.
  • Interface design must offer flexibility to accommodate physician workflows and preferences.
  • Correlated measures offer valuable insights for optimizing user interaction in segmentation tools.