Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes.
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...
| Publicado en: | Ultrasound in Obstetrics & Gynecology Vol. 60; no. 4; pp. 570 - 577 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159454371&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159454371 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09607692 F0Y jtl: Ultrasound in Obstetrics & Gynecology issn: 09607692 maglogo: Y pubinfo: dt: Oct2022 vid: 60 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159454371 159454371 NLM34767663 10.1002/uog.24810 NLM34767663 159454371 ppf: 570 ppct: 7 formats: tig: atl: Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes. aug: au: van den Noort, F. Manzini, C. van der Vaart, C. H. van Limbeek, M. A. J. Slump, C. H. Grob, A. T. M. affil: Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, The Netherlands sug: subj: Obstetrics Pelvic Organ Prolapse Female Imaging, Three-Dimensional Methods Algorithms Ultrasonography Methods Pregnancy Scales Female ab: 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. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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