{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T20:00:06Z","timestamp":1785700806059,"version":"3.56.0"},"reference-count":46,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"am","delay-in-days":365,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#am"},{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000153","name":"Division of Biological Infrastructure","doi-asserted-by":"publisher","award":["DBI 1759883"],"award-info":[{"award-number":["DBI 1759883"]}],"id":[{"id":"10.13039\/100000153","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["NICHD\u2010HD104435"],"award-info":[{"award-number":["NICHD\u2010HD104435"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["DE021417"],"award-info":[{"award-number":["DE021417"]}],"id":[{"id":"10.13039\/100000072","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000105","name":"Office of Advanced Cyberinfrastructure","doi-asserted-by":"publisher","award":["OAC\u20102118240"],"award-info":[{"award-number":["OAC\u20102118240"]}],"id":[{"id":"10.13039\/100000105","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Anatomy"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time\u2010consuming, labor\u2010intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question. Any adjustment to this hypothesis necessitates acquiring a new set of landmarks or revising them significantly, which can be impractical for large datasets. There is a pressing need for more efficient and flexible methods for landmark placement that can adapt to different hypotheses without requiring extensive human effort. This study investigates the precision and accuracy of landmarks derived from functional correspondences obtained through the functional map framework of geometry processing. We utilize a deep functional map network to learn shape descriptors, which enable us to achieve functional map\u2010based and point\u2010to\u2010point correspondences between specimens in our dataset. Our methodology involves automating the landmarking process by interrogating these maps to identify corresponding landmarks, using manually placed landmarks from the entire dataset as a reference. We apply our method to a dataset of rodent mandibles and compare its performance to MALPACA's, a standard tool for automatic landmark placement. Our model demonstrates a speed improvement compared to MALPACA while maintaining a competitive level of accuracy. Although MALPACA typically shows the lowest RMSE, our models perform comparably well, particularly with smaller training datasets, indicating strong generalizability. Visual assessments confirm the precision of our automated landmark placements, with deviations consistently falling within an acceptable range for MALPACA estimates. Our results underscore the potential of unsupervised learning models in anatomical landmark placement, presenting a practical and efficient alternative to traditional methods. Our approach saves significant time and effort and provides the flexibility to adapt to different hypotheses about critical geometrical features without the need for manual re\u2010acquisition of landmarks. This advancement can significantly enhance the scalability and applicability of geometric morphometrics, making it more feasible for large datasets and diverse biological studies.<\/jats:p>","DOI":"10.1111\/joa.14196","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T01:57:45Z","timestamp":1736992665000},"page":"829-845","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging descriptor learning and functional map\u2010based shape matching for automated anatomical Landmarking in mouse mandibles"],"prefix":"10.1111","volume":"246","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3500-5445","authenticated-orcid":false,"given":"Oshane O.","family":"Thomas","sequence":"first","affiliation":[{"name":"Center for Development Biology and Regenerative Medicine Seattle Children's Research Institute  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7921-9018","authenticated-orcid":false,"given":"A. Murat","family":"Maga","sequence":"additional","affiliation":[{"name":"Center for Development Biology and Regenerative Medicine Seattle Children's Research Institute  Seattle Washington USA"},{"name":"Division of Craniofacial Medicine, Department of Pediatrics University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/2041\u2010210x.12035"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/11250000409356545"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-010-0416-3"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/ede.12033"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ar.23084"},{"key":"e_1_2_10_7_1","first-page":"1257","article-title":"Exploration of the genetic organization of morphological modularity on the mouse mandible using a set of interspecific recombinant congenic strains between C57BL\/6 and mice of the Mus spretus species. G3: genes| genomes|","volume":"2","author":"Burgio G.","year":"2012","journal-title":"Genetics"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11692-020-09508-8"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"Donati N. Corman E.&Ovsjanikov M.(2022)Deep orientation\u2010aware functional maps: Tackling symmetry issues in shape matching. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 742\u2013751).","DOI":"10.1109\/CVPR52688.2022.00082"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/ar.24544"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.05.001"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","unstructured":"Halimi O. Litany O. Rodol\u00e0 E. Bronstein A.&Kimmel R.(2018)Self\u2010supervised Learning of Dense Shape Correspondence.","DOI":"10.1109\/CVPR.2019.00450"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10592-021-01369-8"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14084"},{"key":"e_1_2_10_15_1","unstructured":"Kingma D.P.(2014)Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/157.2.785"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469\u20107998.2009.00620.x"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/hdy.2008.79"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym7010146"},{"key":"e_1_2_10_20_1","doi-asserted-by":"crossref","unstructured":"Li S. Zhao Y. Varma R. Salpekar O. Noordhuis P. Li T.et\u00a0al. (2020)Pytorch distributed: Experiences on accelerating data parallel training. arXiv preprint arXiv:2006.15704.","DOI":"10.14778\/3415478.3415530"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.603"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1142\/9789813225701_0008"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/467154a"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2015.00092"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/joa.12645"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajpa.24531"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1534\/g3.115.024372"},{"key":"e_1_2_10_28_1","unstructured":"Ovsjanikov M.(2017)Functional view of geometry processing: Operator\u2010based techniques for shape analysis. Diss. 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