Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes

F. van den Noort*, C. Manzini, C. H. van der Vaart, M. A.J. van Limbeek, C. H. Slump, A. T.M. Grob

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
7 Downloads (Pure)

Abstract

Objective: To develop and validate a tool for automatic selection of the slice of minimal hiatal dimensions (SMHD) and segmentation of the urogenital hiatus (UH) in transperineal ultrasound (TPUS) volumes. Methods: Manual selection of the SMHD and segmentation of the UH was performed in TPUS volumes of 116 women with symptomatic pelvic organ prolapse (POP). These data were used to train two deep-learning algorithms. The first algorithm was trained to provide an estimation of the position of the SMHD. Based on this estimation, a slice was selected and fed into the second algorithm, which performed automatic segmentation of the UH. From this segmentation, measurements of the UH area (UHA), anteroposterior diameter (APD) and coronal diameter (CD) were computed automatically. The mean absolute distance between manually and automatically selected SMHD, the overlap (dice similarity index (DSI)) between manual and automatic UH segmentation and the intraclass correlation coefficient (ICC) between manual and automatic UH measurements were assessed on a test set of 30 TPUS volumes. Results: The mean absolute distance between manually and automatically selected SMHD was 0.20 cm. All DSI values between manual and automatic UH segmentations were above 0.85. The ICC values between manual and automatic UH measurements were 0.94 (95% CI, 0.87–0.97) for UHA, 0.92 (95% CI, 0.78–0.97) for APD and 0.82 (95% CI, 0.66–0.91) for CD, demonstrating excellent agreement. Conclusions: Our deep-learning algorithms allowed reliable automatic selection of the SMHD and UH segmentation in TPUS volumes of women with symptomatic POP. These algorithms can be implemented in the software of TPUS machines, thus reducing clinical analysis time and simplifying the examination of TPUS data for research and clinical purposes.

Original languageEnglish
Pages (from-to)570-576
Number of pages7
JournalUltrasound in Obstetrics and Gynecology
Volume60
Issue number4
DOIs
Publication statusPublished - Oct 2022

Keywords

  • automatic segmentation
  • deep learning
  • levator hiatus
  • pelvic floor
  • transperineal ultrasound
  • urogenital hiatus
  • UT-Hybrid-D

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