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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.

Image de-noising is one of the main steps in the medical image analysis process. In medical imaging, noise usually occurs at the capture stage of medical machines such as the ultrasound machines. This noise may hide important information that affects the diagnosing process. Current medical image de-noising techniques still need modifications to enhance their de-noising capabilities, especially traditional parameter dependent techniques such as VisuShrink. This technique has a threshold that needs to be adjusted to efficiently de-noise the images. In this paper, an intelligent framework is proposed to assign a threshold to VisuShrink technique based on the current image features. These features extracted from the image using Scale Invariant-Feature Transform (SIFT) technique are used to train different machine learning (ML) techniques for predicting the appropriate threshold. The experimental results showed that the proposed framework managed to reduce the noise compared to VisuShrink technique with a fixed threshold.

Importance Vaginal pessaries are an effective nonsurgical treatment for pelvic organ prolapse (POP) when properly fitted. However, pessary fitting and use are often unsuccessful or imperfect. Objective The objective of this study was to assess the feasibility of using patient-specific pessaries fabricated from three-dimensional (3D)-printed molds to improve POP symptoms and increase overall satisfaction of pessary treatment in patients using standard vaginal pessaries. Study Design Patients undergoing POP treatment with standard vaginal pessaries were enrolled in this pilot prospective study. Patient-specific pessaries were designed and fabricated for each patient using patient input, physician input, and anatomic measurements from clinical assessment. Pessary fabrication involved injection of biocompatible liquid silicone rubber into 3D-printed molds followed by a biocompatible silicone coating. Pelvic organ prolapse symptomatic distress and pessary treatment satisfaction were evaluated before and after a 3-week patient-specific pessary home trial using the validated Pelvic Organ Prolapse Distress Inventory-6 form and a visual analog scale, respectively. Results Eight women were included in this study. Changing from standard pessary to patient-specific pessary treatment was associated with an improvement in prolapse symptoms on the Pelvic Organ Prolapse Distress Inventory-6 (median change, −3.5; interquartile range, −5 to −2.5; P = 0.02) and an increase in overall pessary satisfaction on a visual analog scale (median change, +2.0; interquartile range, +1.0 to +3.0; P = 0.02). All patients reported either an improvement or no change in pessary ease of use, comfort, and the feeling of support provided by the pessary. Conclusion Patient-specific vaginal pessaries are a promising alternative to standard pessaries for alleviating POP symptoms and improving patient satisfaction with pessary use.

Three-dimensional (3D) transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Patients with POP have abnormal descent of one or more pelvic organs (i.e., bladder, uterus, vagina) through the levator hiatus, which is often experienced by the patient as a persistent bothersome bulge protruding from the vaginal opening. The enlargement of the hiatal opening measured in the plane of minimal hiatal dimensions (PMHD), has been used as an indication for POP severity. Manually measuring the size of the levator hiatus in 3D TPUS images can be challenging and requires expertise and training and is timeconsuming. Hence a fully automated method for estimating the dimensions of hiatal opening is highly desirable. To this end, we developed a fully automated method to segment the levator hiatus from the PMHD based on the nnU-Net model framework. We trained, validated, and tested on a total of 252 3D US images from 138 patients that may have POP as determined by the pelvic organ prolapse quantification (POP-Q) system. As a benchmark comparison, we compared the nnU-Net to a vanilla U-Net whose hyperparameters were manually tuned. Model performance was determined using Dice similarity coefficient (DSC) and levator hiatus width, length, and area by comparing the model segmentations to manual segmentations. The nnU-Net achieved a DSC of 93.1%±3.3%, absolute width difference of 2.3mm±1.7mm, absolute length difference of 2.6mm±2.5mm and absolute area difference of 1.8cm2±1.3cm2.