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A reporting tool was then developed to integrate the segmentation outputs into an automated report, providing volumetric measurements, fetal weight estimations, and z\u2010score visualizations. The complete pipeline was subsequently deployed on a 0.55T MRI scanner, enabling real\u2010time inference and fully automated reporting in the duration of the acquisition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The segmentation pipeline was quantitatively and retrospectively evaluated on 36 stacks of 18 fetal subjects and demonstrated sufficient performance for all labels, with high scores (0.98) for the fetus, placenta, and amniotic fluid, and 0.91 for the umbilical cord. The prospective evaluation of the scanner deployment step was successfully performed on 50 cases, with the regional volumetric reports available directly on the scanner.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This work demonstrated the feasibility of multi\u2010regional intra\u2010uterine segmentation, fetal weight estimation, and automated reporting in real\u2010time. This study provides a robust baseline solution for the integration of fully automated scanner\u2010based measurements into fetal MRI reports.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70097","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T18:25:06Z","timestamp":1758738306000},"page":"975-986","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Scanner\u2010based real\u2010time automated volumetry reporting of the fetus, amniotic fluid, placenta, and umbilical cord for fetal MRI at 0.55T"],"prefix":"10.1002","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7520-081X","authenticated-orcid":false,"given":"Sara","family":"Neves Silva","sequence":"first","affiliation":[{"name":"Research Department of Early Life Imaging School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Biomedical Computing Department 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