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Related Experiment Video

Updated: Sep 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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An interactive deep-learning workflow for head and neck gross tumour volume segmentation.

Zixiang Wei1,2, Jintao Ren1,2, Jesper Grau Eriksen3,4

  • 1Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.

Physics and Imaging in Radiation Oncology
|August 14, 2025
PubMed
Summary

This study introduces an interactive deep learning (iDL) workflow for head and neck cancer (HNC) segmentation, improving accuracy and usability. The iDL approach integrates clinician input for precise gross tumour volume delineation in radiotherapy.

Keywords:
Head and neck cancerInteractive deep-learningObserver study

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Area of Science:

  • Medical imaging and radiation oncology
  • Artificial intelligence in healthcare
  • Computational anatomy and segmentation

Background:

  • Deep learning (DL) auto-segmentation for head and neck cancer (HNC) gross tumour volumes faces challenges in accuracy and anatomical complexity.
  • Existing methods often require significant manual correction, impacting clinical workflow efficiency.

Purpose of the Study:

  • To develop and evaluate an interactive DL (iDL) workflow integrating clinician input to enhance segmentation performance and usability for HNC gross tumour volumes.
  • To improve the accuracy and efficiency of tumour delineation in radiotherapy planning.

Main Methods:

  • Two iDL approaches were developed: one for primary tumour (GTVt) segmentation using clinician-marked centres and slices for fine-tuning a 3D UNet, and another for lymph node (GTVn) segmentation using clinician clicks as attention maps.
  • The workflow was evaluated using simulations on internal and HECKTOR 2022 datasets, assessing Dice-Similarity-Coefficient (DSCagg).
  • An observer study with radiation oncologists evaluated usability and efficiency using normalized added path length (APL) and System Usability Scale (SUS).

Main Results:

  • The iDL workflow demonstrated high segmentation accuracy, achieving DSCagg of 0.84-0.88 for GTVt and 0.83-0.85 for GTVn across datasets.
  • Minimal corrections were needed for GTVn (mean APL: 4-11%), with limited corrections for GTVt (mean APL: 6-39%).
  • Mean segmentation time was 12 minutes per case, and SUS scores indicated high usability (87.5-100).

Conclusions:

  • The interactive DL workflow provides a practical and efficient solution for head and neck cancer segmentation in radiotherapy.
  • High accuracy, usability, and reduced correction time make the iDL approach a valuable tool for clinical application.