ArticleUltrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology2022
Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes.
Article in Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Supported Analysis of Anal Sphincter and Levator Ani Muscle Using Medical Imaging Techniques: A Systematic Review.International urogynecology journal · 2026Pooled it
- Automated extraction of the plane of minimal hiatal dimensions and mid-sagittal plane from 3D transperineal ultrasound.Medical physics · 2026Article
- The Evolution of Pelvic Floor Ultrasound: From Urinary Incontinence and Pelvic Organ Prolapse to Maternal Birth Trauma, Levator Avulsion and Anal Sphincter Tears.International urogynecology journal · 2026Review
- Artificial Intelligence to Determine Correct Midsagittal Plane in Dynamic Transperineal Ultrasound.Journal of clinical ultrasound : JCU · 2025Observational
- Automated Evaluation of Female Pelvic Organ Descent on Transperineal Ultrasound: Model Development and Validation.International urogynecology journal · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane.International urogynecology journal · 2024Observational
- Artificial intelligence as a teaching tool for gynaecological ultrasound: A systematic search and scoping review.Australasian journal of ultrasound in medicine · 2024Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images.Bioengineering (Basel, Switzerland) · 2023Article
- Automatic segmentation of the female pelvic floor muscles on MRI for pelvic floor function assessment.Quantitative imaging in medicine and surgery · 2023Article
- Unsupervised convolutional autoencoders for 4D transperineal ultrasound classification.Journal of medical imaging (Bellingham, Wash.) · 2023Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
methodsManual 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.
resultsThe 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.
conclusionsOur 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.
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