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Automated segmentation pipelines were developed for total lung volume (TLV) and percent predicted lung volume (PPLV). Performance was assessed using Dice similarity coefficient (DSC), absolute percentage error (APE), Bland-Altman analysis, intraclass correlation coefficient (ICC), case-level risk-category agreement, and inference time. Lowest-performing TLV outputs were adjudicated by a fetal radiologist to simulate a human-in-the-loop workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The final dataset included 906 fetal MRI examinations with complete and recoverable lung segmentations, acquired at a mean gestational age of 27.1\u00a0weeks, range 16-38\u00a0weeks. Automated TLV estimation showed excellent agreement with manual volumetry (ICC, 0.955; mean DSC, 0.81\u00b10.10; median APE 11.1%). Automated PPLV estimation showed lower performance (ICC, 0.727; mean DSC, 0.76\u00b10.12; median APE 14.5%). Mean inference time was 4\u00a0s per examination. Correction of the 25 lowest-performing TLV outputs required 3.1\u00a0min per case.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Automated fetal MRI lung segmentation enables rapid TLV estimation across diverse pulmonary hypoplasia phenotypes and may improve clinical workflow efficiency while preserving expert oversight for challenging cases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00247-026-06765-1","type":"journal-article","created":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T11:14:47Z","timestamp":1788606887000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated fetal magnetic resonance imaging lung segmentation for rapid lung volume estimation across diverse pulmonary hypoplasia phenotypes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1682-7833","authenticated-orcid":false,"given":"Jungwhan John","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shruti","family":"Hegde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5189-9568","authenticated-orcid":false,"given":"Jonathan","family":"Dudley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura A.","family":"Galganski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beth M.","family":"Kline-Fath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0440-5238","authenticated-orcid":false,"given":"Elanchezhian","family":"Somasundaram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0124-0164","authenticated-orcid":false,"given":"Jonathan R.","family":"Dillman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,5]]},"reference":[{"key":"6765_CR1","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.siny.2017.06.004","volume":"22","author":"CM Cotten","year":"2017","unstructured":"Cotten CM (2017) Pulmonary hypoplasia. 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