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Currently, calculating measurements of anatomical structures and relationships as well as extracting the mid\u2010sagittal (MS) plane of 2D and 3D ultrasound images are obtained manually, which is a time\u2010consuming process and requires a reviewer with prior training in pelvic floor US interpretation. The need for manual analysis of ultrasound images has limited the broader adoption of TPUS for evaluating pelvic floor disorders in both research and clinical practice. An automated segmentation and plane extraction method would improve the ability to easily quantify pelvic anatomy relevant to pelvic floor disorders and improve the efficiency and reproducibility of POP diagnosis and treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a fast, reproducible, and automated method of acquiring the MS plane, plane of minimal hiatal dimensions (PMHD), and segmentations of the pelvic floor organs from 3D TPUS images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Our method used a nnU\u2010Net segmentation model to segment structures of interest in the 3D TPUS images. The model segmented the pubis symphysis (PS), urethra, bladder, rectum, rectal ampulla, and anorectal angle (ANA). The segmented output was then fed into a heuristics\u2010based method to determine the PS and ANA to extract the MS plane and PMHD automatically. We used a dataset consisting of 161 3D TPUS images from 104 patients. 89 of the volumes were acquired in a resting state and 72 during the Valsalva maneuver. The segmentation and plane extraction algorithms were evaluated by comparing the results with manual segmentations and manual plane extraction methods using the dice similarity coefficients (DSC), mean absolute surface distance (MAD), and absolute angle difference (AAD), respectively. The Wilcoxon\u2010signed rank statistical test was used with Bonferroni\u2010correction to\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01. Cohen effect size was used for comparing model results.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The nnU\u2010Net segmentation model reported an average DSC(%) of 70.4%, 58.5%, 57.1%, 48.9%, 39.0%, and 19.8% for bladder, rectum, PS, urethra, ANA, and rectal ampulla respectively. The nnU\u2010Net segmentation model achieved significantly higher DSC (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01) for the urethra and rectum than all other tested models. Across all metrics, the nnU\u2010Net segmentation model achieved an average effect size of 0.3, 0.5, 0.7, and 0.8 compared to a 3D ResNet34 + U\u2010Net, 3D U\u2010Net, 2D U\u2010Net, and Attention 3D U\u2010Net model, respectively. The average AADs between the automatically calculated plane slices and manually estimated planes dataset for the MS plane and PMHD were 3.8\u00b0 and 2.4\u00b0, respectively. The PS and ANA segmentation centroids were used to calculate the MS plane and PMHD and they had distance errors of 3.6\u00a0mm and 4.4\u00a0mm.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed an automated 3D segmentation and multiple plane extraction method of female pelvic floor 3D US images. Our method extracts the MS plane and PMHD from 3D US images. The proposed algorithm pipeline can improve the efficiency and reproducibility of TPUS analysis for pelvic floor disorder diagnosis and treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70473","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T02:41:09Z","timestamp":1778467269000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated extraction of the plane of minimal hiatal dimensions and mid\u2010sagittal plane from 3D transperineal ultrasound"],"prefix":"10.1002","volume":"53","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":"R. 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