{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:03:57Z","timestamp":1783029837193,"version":"3.54.6"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Werner Siemens Foundation"},{"name":"Innovation Focus Regenerative Surgery, University Hospital Basel"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Skull segmentation in magnetic resonance imaging (MRI) is essential for cranio-maxillofacial (CMF) surgery planning, yet manual approaches are time-consuming and error-prone. Computed tomography (CT) provides superior bone contrast but exposes patients to ionizing radiation, which is particularly concerning in pediatric care. This study presents an AI-based workflow that enables skull segmentation directly from routine MRI. Using 186 paired CT\u2013MRI datasets, CT-based segmentations were transferred to MRI via multimodal registration to train dedicated deep learning models. Performance was evaluated against manually segmented CT ground truth using Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD), and Hausdorff Distance (HD). AI achieved higher performance on CT (DSC 0.981) than MRI (DSC 0.864), with MSD and HD also favoring CT. Despite lower absolute accuracy on MRI, the approach substantially improved segmentation quality compared with manual MRI methods, particularly in clinically relevant regions. This automated method enables accurate skull modeling from standard MRI without radiation exposure or specialized sequences. While CT remains more precise, the presented framework enhances MRI utility in surgical planning, reduces manual workload, and supports safer, patient-specific treatment, especially for pediatric and trauma cases.<\/jats:p>","DOI":"10.3390\/jimaging11110372","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:03:51Z","timestamp":1761116631000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Redefining MRI-Based Skull Segmentation Through AI-Driven Multimodal Integration"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5377-1530","authenticated-orcid":false,"given":"Michel","family":"Beyer","sequence":"first","affiliation":[{"name":"Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, 4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Aigner","sequence":"additional","affiliation":[{"name":"Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, 4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"},{"name":"Sarcoma Centre, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2995-9878","authenticated-orcid":false,"given":"Alexandru","family":"Burde","sequence":"additional","affiliation":[{"name":"Department of Dental Technology, Faculty of Nursing and Life Sciences, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Brasse","sequence":"additional","affiliation":[{"name":"Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, 4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sead","family":"Abazi","sequence":"additional","affiliation":[{"name":"Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, 4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas B.","family":"Seifert","sequence":"additional","affiliation":[{"name":"Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, 4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob","family":"Wasserthal","sequence":"additional","affiliation":[{"name":"Clinic of Radiology and Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Segeroth","sequence":"additional","affiliation":[{"name":"Clinic of Radiology and Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Omar","sequence":"additional","affiliation":[{"name":"Sarcoma Centre, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3035-9308","authenticated-orcid":false,"given":"Florian M.","family":"Thieringer","sequence":"additional","affiliation":[{"name":"Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, 4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1055\/s-0042-1756451","article-title":"Black Bone MRI for Virtual Surgical Planning in Craniomaxillofacial Surgery","volume":"36","author":"Vyas","year":"2022","journal-title":"Semin. Plast. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20160407","DOI":"10.1259\/dmfr.20160407","article-title":"\u201cBlack Bone\u201d MRI: A novel imaging technique for 3D printing","volume":"46","author":"Eley","year":"2017","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s11517-018-1936-7","article-title":"Automated segmentation of trabecular and cortical bone from proton density weighted MRI of the knee","volume":"57","author":"Chen","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1097\/RLI.0000000000000696","article-title":"Developments in X-Ray Contrast Media and the Potential Impact on Computed Tomography","volume":"55","author":"Jost","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.acra.2021.03.010","article-title":"Automatic Segmentation of Bone Selective MR Images for Visualization and Craniometry of the Cranial Vault","volume":"29","author":"Zimmerman","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/S0140-6736(12)60815-0","article-title":"Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: A retrospective cohort study","volume":"380","author":"Pearce","year":"2012","journal-title":"Lancet"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"794","DOI":"10.4329\/wjr.v6.i10.794","article-title":"Recent advances in imaging technologies in dentistry","volume":"6","author":"Shah","year":"2014","journal-title":"World J. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1111\/prd.12161","article-title":"Use of cone beam computed tomography in implant dentistry: Current concepts, indications and limitations for clinical practice and research","volume":"73","author":"Bornstein","year":"2017","journal-title":"Periodontology 2000"},{"key":"ref_9","unstructured":"Whaites, E., and Drage, N. (2020). Essentials of Dental Radiography and Radiology, Elsevier Health Sciences."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ludwig, U., Eisenbeiss, A.-K., Scheifele, C., Nelson, K., Bock, M., Hennig, J., Von Elverfeldt, D., Herdt, O., Fl\u00fcgge, T., and H\u00f6vener, J.-B. (2016). Dental MRI using wireless intraoral coils. Sci. Rep., 6.","DOI":"10.1038\/srep23301"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4616","DOI":"10.1007\/s00330-016-4254-1","article-title":"Magnetic resonance imaging of intraoral hard and soft tissues using an intraoral coil and FLASH sequences","volume":"26","author":"Ludwig","year":"2016","journal-title":"Eur. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2625","DOI":"10.1007\/s00784-019-03120-7","article-title":"MRI of the inferior alveolar nerve and lingual nerve\u2014Anatomical variation and morphometric benchmark values of nerve diameters in healthy subjects","volume":"24","author":"Burian","year":"2020","journal-title":"Clin. Oral Investig."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1159\/000501901","article-title":"T2 mapping as a tool for assessment of dental pulp response to caries progression: An in vivo MRI study","volume":"54","author":"Cankar","year":"2020","journal-title":"Caries Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41368-018-0017-y","article-title":"Differentiation of periapical granulomas and cysts by using dental MRI: A pilot study","volume":"10","author":"Juerchott","year":"2018","journal-title":"Int. J. Oral Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Algar\u00edn, J.M., D\u00edaz-Caballero, E., Borreguero, J., Galve, F., Grau-Ruiz, D., Rigla, J.P., Bosch, R., Gonz\u00e1lez, J.M., Pall\u00e1s, E., and Corber\u00e1n, M. (2020). Simultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-78456-2"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20120321","DOI":"10.1259\/dmfr.20120321","article-title":"Ultrashort echo time (UTE) MRI for the assessment of caries lesions","volume":"42","author":"Bracher","year":"2013","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2616","DOI":"10.1002\/mrm.28312","article-title":"Two-dimensional UTE overview imaging for dental application","volume":"84","author":"Stumpf","year":"2020","journal-title":"Magn. Reson. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1002\/nbm.2783","article-title":"High-resolution ZTE imaging of human teeth","volume":"25","author":"Weiger","year":"2012","journal-title":"NMR Biomed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.joen.2011.02.022","article-title":"Dental magnetic resonance imaging: Making the invisible visible","volume":"37","author":"Idiyatullin","year":"2011","journal-title":"J. Endod."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Saba, L. (2016). Artifacts in magnetic resonance imaging. Image Principles, Neck, and the Brain, CRC Press. [1st ed.].","DOI":"10.1201\/b19609"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40478-023-01509-w","article-title":"AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study","volume":"11","author":"Wang","year":"2023","journal-title":"Acta Neuropathol. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.neuroimage.2018.03.001","article-title":"Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art","volume":"174","author":"Nielsen","year":"2018","journal-title":"Neuroimage"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104475","DOI":"10.1016\/j.jdent.2023.104475","article-title":"Deep learning for automated segmentation of the temporomandibular joint","volume":"132","author":"Vinayahalingam","year":"2023","journal-title":"J. Dent."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1093\/dmfr\/twae059","article-title":"Automated tooth segmentation in magnetic resonance scans using deep learning\u2014A pilot study","volume":"54","author":"Vinayahalingam","year":"2025","journal-title":"Dentomaxillofac. Radiol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:22:20Z","timestamp":1761117740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":25,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["jimaging11110372"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11110372","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]}}}