{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T18:08:41Z","timestamp":1788890921425,"version":"build-2803163510"},"reference-count":74,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01HD104435"],"award-info":[{"award-number":["P01HD104435"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R03DE031313"],"award-info":[{"award-number":["R03DE031313"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["S10OD032302"],"award-info":[{"award-number":["S10OD032302"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Image registration-based volumetric morphometrics have emerged as a valuable method for identifying subtle morphological differences in neuroimaging and other biomedical images. However, accurate registration out-of-the-box remains challenging when overt morphological phenotypes are present, limiting the application of registration-based morphometrics in developmental and comparative studies where overt phenotypic differences are common. A new label-informed image registration function developed in the ANTsX ecosystem provides an easy to use, generalizable solution for anatomy-aware registration of a wider diversity of morphological variation including many overt phenotypes. In this approach, segmentations (\n                    <jats:italic>i.e.<\/jats:italic>\n                    , labels) provide\n                    <jats:italic>a priori<\/jats:italic>\n                    regional correspondences that guide the registration, allowing morphological experts to define regions of correspondence based on biological concepts of homology (\n                    <jats:italic>e.g.,<\/jats:italic>\n                    tissue origin, gene expression patterns). Here we demonstrate the utility of this label-informed image registration approach for registering knockout mouse embryos with overt phenotypes which fail to register to a wildtype (normative) template image by traditional registration methods. Due to severe scoliosis, E15.5\n                    <jats:italic>Gli2<\/jats:italic>\n                    <jats:sup>\n                      <jats:italic>-\/-<\/jats:italic>\n                    <\/jats:sup>\n                    mouse embryos exhibit a radical topological rearrangement of the internal organs; traditional intensity-only registration fails to accurately align the organs of knockout embryos with the normative template, limiting the interpretability of registration-based morphometric analyses. In contrast, label-informed image registration improved the correspondence of knockout subjects to the canonical template image, increasing the biological interpretability, power, and sensitivity of registration-derived morphometrics. All in all, label-informed image registration provides a flexible and customizable method to allow image registration in datasets for which registration-based morphometrics were previously unfeasible, unlocking new potential applications of registration-based morphometrics in developmental, comparative, and evolutionary studies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0357448","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T17:36:23Z","timestamp":1788888983000},"page":"e0357448","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anatomy-aware, label-informed approach improves image registration for challenging datasets"],"prefix":"10.1371","volume":"21","author":[{"given":"Rachel A.","family":"Roston","sequence":"first","affiliation":[{"name":"Center for Developmental Biology and Regenerative Medicine, Seattle Children\u2019s Research Institute","place":["Seattle, Washington, United States of America"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-5103","authenticated-orcid":true,"given":"Nicholas J.","family":"Tustison","sequence":"additional","affiliation":[{"name":"University of Virginia","place":["Charlottesville, Virginia, United States of America"],"department":["Department of Radiology and Medical Imaging"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7921-9018","authenticated-orcid":true,"given":"A. 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