Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.

Journal: Computers in biology and medicine
Published Date: May 28, 2024

Abstract

BACKGROUND: The different tumor appearance of head and neck cancer across imaging modalities, scanners, and acquisition parameters accounts for the highly subjective nature of the manual tumor segmentation task. The variability of the manual contours is one of the causes of the lack of generalizability and the suboptimal performance of deep learning (DL) based tumor auto-segmentation models. Therefore, a DL-based method was developed that outputs predicted tumor probabilities for each PET-CT voxel in the form of a probability map instead of one fixed contour. The aim of this study was to show that DL-generated probability maps for tumor segmentation are clinically relevant, intuitive, and a more suitable solution to assist radiation oncologists in gross tumor volume segmentation on PET-CT images of head and neck cancer patients.

Authors

  • Alessia de Biase
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, GR, The Netherlands.
  • Liv Ziegfeld
    University of Groningen, University of Groningen (RUG), 9700 AK, Groningen, the Netherlands.
  • Nanna Maria Sijtsema
    Department of Radiation Oncology, University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands.
  • Roel Steenbakkers
    Department of Radiation Oncology, University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands.
  • Robin Wijsman
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Lisanne V van Dijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: l.v.van.dijk@umcg.nl.
  • Johannes A Langendijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Fokie Cnossen
    Department of Artificial Intelligence, 84790University of Groningen, The Netherlands.
  • Peter van Ooijen
    Department of Radiation Oncology, Coordinator Machine Learning Lab, Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.

Keywords

External Resources

Popular Topics
Recent Journals