{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T23:41:21Z","timestamp":1790811681539,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000196","name":"Canada Foundation for Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013873","name":"Government of Ontario","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013873","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003579","name":"University of Toronto","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003579","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Transperineal 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\u2010sagittal plane of 2D and 3D US volumes are obtained manually, which is time\u2010consuming, has high intra\u2010rater variability, and requires an expert in pelvic floor US interpretation. Manual segmentation and biometric measurement can take 15 min per 2D mid\u2010sagittal 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\u2010based biometric methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Develop a fast, reproducible, and automated method of acquiring biometric measurements and organ segmentations from the mid\u2010sagittal plane of female 3D TPUS volumes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our method used a nnU\u2010Net segmentation model to segment the pubis symphysis, urethra, bladder, rectum, rectal ampulla, and anorectal angle in the mid\u2010sagittal 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\u2010class 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\u2010test and non\u2010parametric Wilcoxon signed\u2010rank test were conducted, with <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05 determining significance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The nnU\u2010Net segmentation model reported average DSC and <jats:italic>p<\/jats:italic> values (in brackets), compared to the next best tested model, of 87.4% (&lt;0.0001), 68.5% (&lt;0.0001), 61.0% (0.1), 54.6% (0.04), 49.2% (&lt;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\u00a0mm, 4.5\u00a0mm, 5.3\u00a0mm and 27.3\u00b0, respectively. The biometric algorithm had an ICC\u00a0&gt;\u00a00.80 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Based 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\u2010sagittal 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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16389","type":"journal-article","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T19:38:39Z","timestamp":1679773119000},"page":"6215-6227","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Automated segmentation and measurement of the female pelvic floor from the mid\u2010sagittal plane of 3D ultrasound volumes"],"prefix":"10.1002","volume":"50","author":[{"given":"Zachary","family":"Szentimrey","sequence":"first","affiliation":[{"name":"School of Engineering University of Guelph  Guelph Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Golafsoun","family":"Ameri","sequence":"additional","affiliation":[{"name":"Cosm Medical  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher X.","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Obstetrics &amp; Gynaecology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel Y. 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