Development and validation of a deep learning-based method for automatic measurement of uterus, fibroid, and ablated volume in MRI after MR-HIFU treatment of uterine fibroids.

Journal: European journal of radiology
PMID: 38991285

Abstract

INTRODUCTION: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ultrasound (MR-HIFU) treatments of uterine fibroids, which is related to long-term symptom relief. In current clinical practice, the MR-HIFU outcome parameters are typically determined by visual inspection, so an automated computer-aided method could facilitate objective outcome quantification. The objective of this study was to develop and evaluate a deep learning-based segmentation algorithm for volume measurements of the uterus, uterine fibroids, and NPVs in MRI in order to automatically quantify the NPV/TFL.

Authors

  • Derk J Slotman
    Department of Radiology, Isala Hospital, Zwolle, The Netherlands. d.j.slotman@isala.nl.
  • Lambertus W Bartels
    Imaging & Oncology Division, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Ingrid M Nijholt
    Department of Radiology and Nuclear Medicine, Isala, P.O. Box 10400, 8000 GK Zwolle, The Netherlands.
  • Judith A F Huirne
    Department of Obstetrics and Gynaecology, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Chrit T W Moonen
    Imaging & Oncology Division, University Medical Center Utrecht, Utrecht, the Netherlands; Focused Ultrasound Foundation, Charlottesville, VA, United States of America.
  • Martijn F Boomsma
    Department of Radiology and Nuclear Medicine, Isala, P.O. Box 10400, 8000 GK Zwolle, The Netherlands.

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