Agreement of Ultrasound Measures with POP-Q in Patients with Pelvic Organ Prolapse

Arian, Arvin; Ghanbari, Zinat; Chegini, Nastaran; Hosseiny, Melina

doi:10.5812/iranjradiol.68461

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…The biometrics of interest are calculated based on previous established studies that showed correlation between these key metrics and degree of POP. [9][10][11][12][13][14][15][16] The manualand algorithm-generated biometrics follow the same established studies.…”

Section: Pelvic Floor Biometricsmentioning

confidence: 99%

“…The location of anatomical structures are currently measured manually from the US volumes, which is time‐consuming and requires a reviewer with prior training in pelvic floor ultrasound interpretation 10–16 . An expert pelvic floor US interpreter may take up to 15 min to manually segment and analyze a single 2D mid‐sagittal plane image.…”

Section: Introductionmentioning

confidence: 99%

“…Automated methods are reproducible, quick, less expensive with regards to human involvement and non‐user dependent compared to manual methods 18 . Previous studies have shown that biometrics from the mid‐sagittal plane including the bladder neck descent, bladder descent and rectal ampulla descent are key metrics from determining the severity of POP 9–16 . Previous studies have calculated the biometrics manually, and no automated biometric extraction method have been reported for female pelvic 3D US.…”

Section: Introductionmentioning

confidence: 99%

“…9 The location of anatomical structures are currently measured manually from the US volumes, which is timeconsuming and requires a reviewer with prior training in pelvic floor ultrasound interpretation. [10][11][12][13][14][15][16] An expert pelvic floor US interpreter may take up to 15 min to manually segment and analyze a single 2D mid-sagittal plane image. Moreover, the time required to analyze a single patient's data increases when additional images per patient are taken during pelvic muscle maneuvers, such as Valsalva, and contraction.…”

mentioning

confidence: 99%

“…18 Previous studies have shown that biometrics from the mid-sagittal plane including the bladder neck descent,bladder descent and rectal ampulla descent are key metrics from determining the severity of POP. [9][10][11][12][13][14][15][16] Previous studies have calculated the biometrics manually, and no automated biometric extraction method have been reported for female pelvic 3D US. However, other applications of 3D US, including prostate and neonatal cerebral ventricle applications, have used automated methods for segmenting and measuring clinically useful biometrics such as ventricle size and prostate volume.…”

mentioning

confidence: 99%

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Automated segmentation and measurement of the female pelvic floor from the mid‐sagittal plane of 3D ultrasound volumes

Szentimrey

Ameri²,

Hong

et al. 2023

Medical Physics

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BackgroundTransperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with pelvic floor disorders, including pelvic organ prolapse (POP). Currently, measurements of anatomical structures in the mid‐sagittal plane of 2D and 3D US volumes are obtained manually, which is time‐consuming, has high intra‐rater variability, and requires an expert in pelvic floor US interpretation. Manual segmentation and biometric measurement can take 15 min per 2D mid‐sagittal image by an expert operator. An automated segmentation method would provide quantitative data relevant to pelvic floor disorders and improve the efficiency and reproducibility of segmentation‐based biometric methods.PurposeDevelop a fast, reproducible, and automated method of acquiring biometric measurements and organ segmentations from the mid‐sagittal plane of female 3D TPUS volumes.MethodsOur method used a nnU‐Net segmentation model to segment the pubis symphysis, urethra, bladder, rectum, rectal ampulla, and anorectal angle in the mid‐sagittal plane of female 3D TPUS volumes. We developed an algorithm to extract relevant biometrics from the segmentations. Our dataset included 248 3D TPUS volumes, 126/122 rest/Valsalva split, from 135 patients. System performance was assessed by comparing the automated results with manual ground truth data using the Dice similarity coefficient (DSC) and average absolute difference (AD). Intra‐class correlation coefficient (ICC) and time difference were used to compare reproducibility and efficiency between manual and automated methods respectively. High ICC, low AD and reduction in time indicated an accurate and reliable automated system, making TPUS an efficient alternative for POP assessment. Paired t‐test and non‐parametric Wilcoxon signed‐rank test were conducted, with p < 0.05 determining significance.ResultsThe nnU‐Net segmentation model reported average DSC and p values (in brackets), compared to the next best tested model, of 87.4% (<0.0001), 68.5% (<0.0001), 61.0% (0.1), 54.6% (0.04), 49.2% (<0.0001) and 33.7% (0.02) for bladder, rectum, urethra, pubic symphysis, anorectal angle, and rectal ampulla respectively. The average ADs for the bladder neck position, bladder descent, rectal ampulla descent and retrovesical angle were 3.2 mm, 4.5 mm, 5.3 mm and 27.3°, respectively. The biometric algorithm had an ICC > 0.80 for the bladder neck position, bladder descent and rectal ampulla descent when compared to manual measurements, indicating high reproducibility. The proposed algorithms required approximately 1.27 s to analyze one image. The manual ground truths were performed by a single expert operator. In addition, due to high operator dependency for TPUS image collection, we would need to pursue further studies with images collected from multiple operators.ConclusionsBased on our search in scientific databases (i.e., Web of Science, IEEE Xplore Digital Library, Elsevier ScienceDirect and PubMed), this is the first reported work of an automated segmentation and biometric measurement system for the mid‐sagittal plane of 3D TPUS volumes. The proposed algorithm pipeline can improve the efficiency (1.27 s compared to 15 min manually) and has high reproducibility (high ICC values) compared to manual TPUS analysis for pelvic floor disorder diagnosis. Further studies are needed to verify this system's viability using multiple TPUS operators and multiple experts for performing manual segmentation and extracting biometrics from the images.

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Section: Pelvic Floor Biometricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Automated segmentation and measurement of the female pelvic floor from the mid‐sagittal plane of 3D ultrasound volumes

Szentimrey

Ameri²,

Hong

et al. 2023

Medical Physics

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Diagnostic value of pelvic floor ultrasonography for diagnosis of pelvic organ prolapse: a systematic review

et al. 2019

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The role of pelvic floor ultrasound correlated with pelvic organ prolapse quantification in the assessment of anterior and apical compartments of pelvic organ prolapse

El-Gohary,

Hamed,

El-Azizi

et al. 2024

Egypt J Radiol Nucl Med

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Background Pelvic organ prolapse (POP) is a gynecological disease significantly associated with older age. A higher prevalence of women with symptomatic POP showed physical and emotional distress, negatively affecting their quality of life (QoL). The most widespread tool used is the prolapse quantification system (POP-Q) of the International Continence Society (ICS). The aim of this study was to evaluate the role of ultrasound (U/S) compared to POP-Q for the detection and quantification of POP in the anterior (urinary bladder) and apical (cervix/vaginal vault) compartments of the pelvic floor in Egyptian women. Results The current study revealed that among 83 women, 53 had POP with a mean age of 50.83 years, 96.2% had anterior compartment prolapse (either alone or with apical compartment prolapse), 52% had apical compartment prolapse (either alone or with anterior compartment prolapse), 47.2% had anterior compartment prolapse only, and 3.7% had apical compartment prolapse only. There was a strong agreement (almost linear) between (POP-Q) and U/S in detecting significant pelvic organ prolapse in the anterior compartment (Kappa value 0.925, P < 0.001) and the apical compartment (Kappa value 0.945 and P < 0.001). With higher value of sensitivity and specificity, our study assigned significant anterior compartment prolapse using a cutoff value of 0 for point Ba of POP-Q and −11.5 for bladder neck descent at valsalva using U/S. Conclusions Pelvic floor ultrasound provides general and detailed anatomical overview of the pelvic floor as well as detection and assessment of the POP in anterior and middle compartments.

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Agreement of Ultrasound Measures with POP-Q in Patients with Pelvic Organ Prolapse

Cited by 3 publications

References 20 publications

Automated segmentation and measurement of the female pelvic floor from the mid‐sagittal plane of 3D ultrasound volumes

Automated segmentation and measurement of the female pelvic floor from the mid‐sagittal plane of 3D ultrasound volumes

Diagnostic value of pelvic floor ultrasonography for diagnosis of pelvic organ prolapse: a systematic review

The role of pelvic floor ultrasound correlated with pelvic organ prolapse quantification in the assessment of anterior and apical compartments of pelvic organ prolapse

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