Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer.

Journal: Scientific reports
Published Date: Aug 5, 2025

Abstract

Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an interactive DL for GTVt segmentation in OPC by introducing a novel two-stage Interactive Click Refinement (2S-ICR) framework and implementing state-of-the-art algorithms. Using the 2021 HEad and neCK TumOR dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.722 ± 0.142 without user interaction and 0.858 ± 0.050 after ten interactions, thus outperforming existing methods in both cases.

Authors

  • Mikko Saukkoriipi
    Department of Computer Science, Aalto University School of Science, Espoo, Finland.
  • Jaakko Sahlsten
    Dept. of Computer Science, Aalto University School of Science, Espoo, 00076, Finland.
  • Joel Jaskari
    Dept. of Computer Science, Aalto University School of Science, Espoo, 00076, Finland.
  • Lotta Orsmaa
    Faculty of Information Technology and Communication Sciences, Computing Sciences, University of Tampere, Tampere, Finland.
  • Jari Kangas
    Faculty of Information Technology and Communication Sciences, Computing Sciences, University of Tampere, Tampere, Finland.
  • Nastaran Rasouli
    Faculty of Information Technology and Communication Sciences, Computing Sciences, University of Tampere, Tampere, Finland.
  • Roope Raisamo
    Faculty of Information Technology and Communication Sciences, Computing Sciences, University of Tampere, Tampere, Finland.
  • Jussi Hirvonen
    Department of Radiology, Turku University Hospital & University of Turku, Kiinamyllynkatu 4-8, 20521 Turku, Finland.
  • Helena Mehtonen
    Medical Imaging Centre, Department of Radiology Tampere University Hospital, Teiskontie 35, 33520, Tampere, Finland.
  • Jorma Järnstedt
    Medical Imaging Centre, Department of Radiology Tampere University Hospital, Teiskontie 35, 33520, Tampere, Finland.
  • Antti Mäkitie
    Department of Otorhinolaryngology-Head and Neck Surgery, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Mohamed Naser
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Clifton Fuller
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Benjamin Kann
    Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Kimmo Kaski
    Dept. of Computer Science, Aalto University School of Science, Espoo, 00076, Finland. kimmo.kaski@aalto.fi.

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