Deep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision.

Journal: Surgical endoscopy
PMID: 39753930

Abstract

BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are not included in the pre-operative difficulty assessment. In this work, we propose an automated workflow for pelvimetry based on pre-operative magnetic resonance imaging (MRI) volumes.

Authors

  • Simon C Baltus
    Surgery Department, Meander Medical Centre, Maatweg, Amersfoort, 3818 TZ, Utrecht, The Netherlands. sc.baltus@meandermc.nl.
  • Ritch T J Geitenbeek
    Surgery Department, Meander Medical Centre, Maatweg, Amersfoort, 3818 TZ, Utrecht, The Netherlands.
  • Maike Frieben
    Surgery Department, University Medical Center Groningen, Hanzeplein, Groningen, 9713 GZ, Groningen, The Netherlands.
  • Elina Thibeau-Sutre
    Paris Brain Institute, in the ARAMIS Lab.
  • Jelmer M Wolterink
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • Can O Tan
    Robotics and Mechatronics, University of Twente, Drienerlolaan, Enschede, 5722 NB, Overijssel, The Netherlands.
  • Matthijs C Vermeulen
    Surgery Department, Meander Medical Centre, Maatweg, Amersfoort, 3818 TZ, Utrecht, The Netherlands.
  • Esther C J Consten
    Jan J van Iersel, Tim JC Paulides, Paul M Verheijen, Ivo AMJ Broeders, Esther CJ Consten, Meander Medical Centre, Department of Surgery, 3813 TZ Amersfoort, The Netherlands.
  • Ivo A M J Broeders
    Department of Robotics and Mechatronics, University of Twente, Enschede, the Netherlands.

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