Automated segmentation of endometrial cancer on MR images using deep learning

Erlend Hodneland, Julie A. Dybvik, Kari S. Wagner-Larsen, Veronika Šoltészová, Antonella Z. Munthe-Kaas, Kristine E. Fasmer, Camilla Krakstad, Arvid Lundervold, Alexander S. Lundervold, Øyvind Salvesen, Bradley J. Erickson, Ingfrid Haldorsen

Research output: Contribution to journalArticlepeer-review

Abstract

Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, p= 0.06). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, p= 0.08 , p= 0.60 , and p= 0.05). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

Original languageEnglish (US)
Article number179
JournalScientific reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

ASJC Scopus subject areas

  • General

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