{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:38:26Z","timestamp":1792787906898,"version":"4.5.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum RWTH Aachen","id":[{"id":"https:\/\/ror.org\/04xfq0f34","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The pelvis is among the most demanding regions in musculoskeletal imaging, and CT is its reference standard. Available automated tools, however, largely produce whole-bone or single-task masks, leaving the finer surgical subregions to slow, operator-dependent manual work. We developed a rule-based pipeline that converts pelvic and proximal femoral masks into a structured map of surgically meaningful subregions and landmarks, quantifying the manual correction burden required to achieve anatomically acceptable outputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Anonymized pelvic CT scans were collected retrospectively; after predefined quality criteria, 757 of 1,316 screened normal adult cases were processed by a one-click pipeline in 3D Slicer. Parent structures from TotalSegmentator were divided by fixed anatomical rules into columns, acetabular regions, and proximal femoral regions. In 100 randomly selected cases, every segment was expert-reviewed and corrected where needed; correction frequency was the primary endpoint, with accuracy computed for corrected segments only. Pipeline performance was also assessed in a 60-case pathological-anatomy cohort for affected segments against manually segmented ground truth.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 8,100 segment observations, 93.8% were accepted without correction, and 51 of 81 segments required no correction in any case. Correction clustered in a few boundary regions, mainly the ischium and femoral neck (median 4.5 segments per case). Computed only on the corrected segments, the median Dice was 0.94, indicating that errors were modest where they occurred. A secondary symmetry analysis showed median surface agreement of 90% within 2\u00a0mm and 99.9% within 5\u00a0mm. In the pathological-anatomy cohort, pooled median Dice was 0.970 across affected components. Pooled landmark error was 5.37\u00a0mm median, with 91.9% of manual clicks inside the automated landmark.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Validated in normal adult anatomy, this rule-based pipeline converts pelvic and proximal femoral CT into a structured hierarchy of clinically relevant subregions, needing only limited, predictable correction. It substantially reduces manual workload while preserving the anatomical detail needed for morphometry and dataset generation. Performance held up in an initial pathological-anatomy sample. Extension to fracture, deformity, or clinical decision-making requires dedicated validation in abnormal anatomy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-026-02753-x","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:43:39Z","timestamp":1788875019000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anatomy-driven automated subsegmentation of pelvic and proximal femoral CT into clinically relevant subregions and landmarks"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6259-7985","authenticated-orcid":false,"given":"Mohammed","family":"Rashed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hatem","family":"Alabdulrahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon D.","family":"Westfechtel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Hildebrand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Truhn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"issue":"5","key":"2753_CR1","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1007\/s10439-014-1176-4","volume":"43","author":"C Chu","year":"2015","unstructured":"Chu C, Chen C, Liu L, Zheng G. FACTS: fully automatic CT segmentation of a hip joint. Ann Biomed Eng. 2015;43(5):1247\u201359. https:\/\/doi.org\/10.1007\/s10439-014-1176-4.","journal-title":"Ann Biomed Eng"},{"issue":"2","key":"2753_CR2","doi-asserted-by":"publisher","first-page":"384","DOI":"10.3390\/diagnostics12020384","volume":"12","author":"E Leone","year":"2022","unstructured":"Leone E, Garipoli A, Ripani U, Lanzetti RM, Spoliti M, Creta D, Giannace C, Galluzzo A, Trinci M, Galluzzo M. Imaging review of pelvic ring fractures and its complications in high-energy trauma. Diagnostics (Basel). 2022;12(2):384. https:\/\/doi.org\/10.3390\/diagnostics12020384.","journal-title":"Diagnostics (Basel)"},{"issue":"8","key":"2753_CR3","doi-asserted-by":"publisher","first-page":"588","DOI":"10.7326\/0003-4819-158-8-201304160-00003","volume":"158","author":"PJ Pickhardt","year":"2013","unstructured":"Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med. 2013;158(8):588\u201395. https:\/\/doi.org\/10.7326\/0003-4819-158-8-201304160-00003.","journal-title":"Ann Intern Med"},{"key":"2753_CR4","doi-asserted-by":"publisher","first-page":"103506","DOI":"10.1016\/j.media.2025.103506","volume":"102","author":"Y Liu","year":"2025","unstructured":"Liu Y, Yibulayimu S, Sang Y, Zhu G, Shi C, Liang C, Cao Q, Zhao C, Wu X, Wang Y. Preoperative fracture reduction planning for image-guided pelvic trauma surgery: a comprehensive pipeline with learning. Med Image Anal. 2025;102:103506. https:\/\/doi.org\/10.1016\/j.media.2025.103506.","journal-title":"Med Image Anal"},{"key":"2753_CR5","doi-asserted-by":"publisher","first-page":"102693","DOI":"10.1016\/j.compmedimag.2025.102693","volume":"127","author":"X Zhao","year":"2026","unstructured":"Zhao X, Xiao D, Zhang T, Fan J, Ai D, Fu T, Lin Y, Shao L, Song H, Wang J, Yang J. Defect-adaptive landmark detection in pelvis CT images via personalized structure-aware learning. Comput Med Imaging Graph. 2026;127:102693. https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102693.","journal-title":"Comput Med Imaging Graph"},{"issue":"11","key":"2753_CR6","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1177\/02841851251359649","volume":"66","author":"S De Raedt","year":"2025","unstructured":"De Raedt S, Bentzen A, Mechlenburg I, Stilling M, R\u00f8mer L, S\u00f8balle K, de Bruijne M. Lunate extract: fully automatic acetabular lunate segmentation and hip angle measurements. Acta Radiol. 2025;66(11):1208\u201316. https:\/\/doi.org\/10.1177\/02841851251359649.","journal-title":"Acta Radiol"},{"key":"2753_CR7","doi-asserted-by":"publisher","unstructured":"Seim H, Kainmueller D, Heller M, Lamecker H, Zachow S, Hege HC. Automatic segmentation of the pelvic bones from CT data based on a statistical shape model. In: Eurographics workshop on visual computing for biomedicine (VCBM). The eurographics association. 2008:93\u2013100. https:\/\/doi.org\/10.2312\/VCBM\/VCBM08\/093-100","DOI":"10.2312\/VCBM\/VCBM08\/093-100"},{"issue":"4","key":"2753_CR8","doi-asserted-by":"publisher","first-page":"3917","DOI":"10.1007\/978-3-540-39899-8_3","volume":"43","author":"J Ehrhardt","year":"2004","unstructured":"Ehrhardt J, Handels H, Pl\u00f6tz W, P\u00f6ppl SJ. Atlas-based recognition of anatomical structures and landmarks and the automatic computation of orthopedic parameters. Methods Inf Med. 2004;43(4):3917. https:\/\/doi.org\/10.1007\/978-3-540-39899-8_3.","journal-title":"Methods Inf Med"},{"issue":"4","key":"2753_CR9","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TITB.2003.813791","volume":"7","author":"RA Zoroofi","year":"2003","unstructured":"Zoroofi RA, Sato Y, Sasama T, Nishii T, Sugano N, Yonenobu K, Yoshikawa H, Ochi T, Tamura S. Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans Inf Technol Biomed. 2003;7(4):329\u201343. https:\/\/doi.org\/10.1109\/TITB.2003.813791.","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"1","key":"2753_CR10","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.media.2015.08.011","volume":"26","author":"C Chu","year":"2015","unstructured":"Chu C, Bai J, Wu X, Zheng G. MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images. Med Image Anal. 2015;26(1):173\u201384. https:\/\/doi.org\/10.1016\/j.media.2015.08.011.","journal-title":"Med Image Anal"},{"issue":"5","key":"2753_CR11","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s11548-021-02363-8","volume":"16","author":"P Liu","year":"2021","unstructured":"Liu P, Han H, Du Y, Zhu H, Li Y, Gu F, Xiao H, Li J, Zhao C, Xiao L, Wu X, Zhou SK. Deep learning to segment pelvic bones: large-scale CT datasets and baseline models. Int J Comput Assist Radiol Surg. 2021;16(5):749\u201356. https:\/\/doi.org\/10.1007\/s11548-021-02363-8.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"5","key":"2753_CR12","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, Heye T, Boll DT, Cyriac J, Yang S, Bach M, Segeroth M. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell. 2023;5(5):e230024. https:\/\/doi.org\/10.1148\/ryai.230024.","journal-title":"Radiol Artif Intell"},{"key":"2753_CR13","doi-asserted-by":"publisher","DOI":"10.1142\/S0219519426400282","author":"T Qin","year":"2026","unstructured":"Qin T, Yuan X, Yan X, Xu L, Liu Y. HGSF-Net: automatic pelvic fracture CT segmentation with 2.5D neighborhood context and adaptive detail guidance. J Mech Med Biol. 2026. https:\/\/doi.org\/10.1142\/S0219519426400282.","journal-title":"J Mech Med Biol"},{"issue":"11","key":"2753_CR14","doi-asserted-by":"publisher","first-page":"3767","DOI":"10.3390\/jcm12113767","volume":"12","author":"WA van Veldhuizen","year":"2023","unstructured":"van Veldhuizen WA, van der Wel H, Kuipers HY, Kraeima J, ten Duis K, Wolterink JM, et al. Development of a statistical shape model and assessment of anatomical shape variations in the hemipelvis. J Clin Med. 2023;12(11):3767. https:\/\/doi.org\/10.3390\/jcm12113767.","journal-title":"J Clin Med"},{"key":"2753_CR15","doi-asserted-by":"publisher","first-page":"101917","DOI":"10.1016\/j.media.2020.101917","volume":"68","author":"R Han","year":"2021","unstructured":"Han R, Uneri A, Vijayan RC, Wu P, Vagdargi P, Sheth N, et al. Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration. Med Image Anal. 2021;68:101917. https:\/\/doi.org\/10.1016\/j.media.2020.101917.","journal-title":"Med Image Anal"},{"issue":"1","key":"2753_CR16","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s12891-025-09444-8","volume":"27","author":"SJ Nesheli","year":"2026","unstructured":"Nesheli SJ, Sabet M, Koozari A, Mirzaghavami P, Eftekhar A, Elhaie M, Rouhi S, Lariche NJ, Abidi M, Rezaeijo SM. Transformer-based and CNN-based models for clinically effective 2D and 3D pelvic bone segmentation in CT imaging. BMC Musculoskelet Disord. 2026;27(1):68. https:\/\/doi.org\/10.1186\/s12891-025-09444-8.","journal-title":"BMC Musculoskelet Disord"},{"key":"2753_CR17","doi-asserted-by":"publisher","unstructured":"Li C, Chen L, Liu Q, Teng J. Lumbar and pelvic CT image segmentation based on cross-scale feature fusion and linear self-attention mechanism. Sci Rep. 2025;15(1). https:\/\/doi.org\/10.1038\/s41598-025-13569-0.","DOI":"10.1038\/s41598-025-13569-0"},{"issue":"1","key":"2753_CR18","doi-asserted-by":"publisher","first-page":"68","DOI":"10.5603\/rpor.109519","volume":"31","author":"K Kosaka","year":"2026","unstructured":"Kosaka K, Ueda Y, Kuroki Y, Minami H, Inui S, Morimoto M, et al. Automatic pelvic bone segmentation for radiotherapy using diagnostic imaging workstation. Rep Pract Oncol Radiother. 2026;31(1):68\u201380. https:\/\/doi.org\/10.5603\/rpor.109519.","journal-title":"Rep Pract Oncol Radiother"},{"key":"2753_CR19","doi-asserted-by":"publisher","first-page":"1511487","DOI":"10.3389\/fmed.2025.1511487","volume":"12","author":"Y Liu","year":"2025","unstructured":"Liu Y, Yibulayimu S, Zhu G, Shi C, Liang C, Zhao C, Wu X, Sang Y, Wang Y. Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset. Front Med (Lausanne). 2025;12:1511487. https:\/\/doi.org\/10.3389\/fmed.2025.1511487.","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"2753_CR20","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/TMI.2024.3429403","volume":"44","author":"J Liu","year":"2025","unstructured":"Liu J, Li H, Zeng B, Wang H, Kikinis R, Joskowicz L, et al. An end-to-end geometry-based pipeline for automatic preoperative surgical planning of pelvic fracture reduction and fixation. IEEE Trans Med Imaging. 2025;44(1):79\u201391. https:\/\/doi.org\/10.1109\/TMI.2024.3429403.","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"2753_CR21","doi-asserted-by":"publisher","first-page":"3567","DOI":"10.3390\/jcm14103567","volume":"14","author":"N Ramadanov","year":"2025","unstructured":"Ramadanov N, Zabler S. Ramadanov-Zabler safe zone for sacroiliac screw placement: a CT-based computational pilot study. J Clin Med. 2025;14(10):3567. https:\/\/doi.org\/10.3390\/jcm14103567.","journal-title":"J Clin Med"},{"key":"2753_CR22","doi-asserted-by":"publisher","first-page":"905","DOI":"10.2147\/TCRM.S521755","volume":"21","author":"TJ Yang","year":"2025","unstructured":"Yang TJ, Qian W. AI-assisted 3D planning of CT parameters for personalized femoral prosthesis selection in total hip arthroplasty. Ther Clin Risk Manag. 2025;21:905\u201316. https:\/\/doi.org\/10.2147\/TCRM.S521755.","journal-title":"Ther Clin Risk Manag"},{"issue":"3","key":"2753_CR23","doi-asserted-by":"publisher","first-page":"e0319907","DOI":"10.1371\/journal.pone.0319907","volume":"20","author":"TJ Yang","year":"2025","unstructured":"Yang TJ, Wen PP, Ye X, Wu XF, Zhang C, Sun SY, et al. CT Hounsfield units in assessing bone and soft tissue quality in the proximal femur: a systematic review focusing on osteonecrosis and total hip arthroplasty. PLoS ONE. 2025;20(3):e0319907. https:\/\/doi.org\/10.1371\/journal.pone.0319907.","journal-title":"PLoS ONE"},{"issue":"4","key":"2753_CR24","doi-asserted-by":"publisher","first-page":"477","DOI":"10.4103\/0366-6999.151088","volume":"128","author":"XB Wu","year":"2015","unstructured":"Wu XB, Wang JQ, Zhao CP, Sun X, Shi Y, Zhang ZA, et al. Printed three-dimensional anatomic templates for virtual preoperative planning before reconstruction of old pelvic injuries: initial results. Chin Med J (Engl). 2015;128(4):477\u201382. https:\/\/doi.org\/10.4103\/0366-6999.151088.","journal-title":"Chin Med J (Engl)"},{"issue":"6","key":"2753_CR25","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s11999-011-1827-9","volume":"469","author":"HJP van Bosse","year":"2011","unstructured":"van Bosse HJP, Lee D, Henderson ER, Sala DA, Feldman DS. Pelvic positioning creates error in CT acetabular measurements. Clin Orthop Relat Res. 2011;469(6):1683\u201391. https:\/\/doi.org\/10.1007\/s11999-011-1827-9.","journal-title":"Clin Orthop Relat Res"},{"issue":"9","key":"2753_CR26","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323\u201341. https:\/\/doi.org\/10.1016\/j.mri.2012.05.001.","journal-title":"Magn Reson Imaging"},{"issue":"3","key":"2753_CR27","doi-asserted-by":"publisher","first-page":"W425","DOI":"10.2214\/AJR.12.10470","volume":"201","author":"DA Lawrence","year":"2013","unstructured":"Lawrence DA, Menn K, Baumgaertner M, Haims AH. Acetabular fractures: anatomic and clinical considerations. AJR Am J Roentgenol. 2013;201(3):W425\u201336. https:\/\/doi.org\/10.2214\/AJR.12.10470.","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"2753_CR28","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s11999-013-3375-y","volume":"472","author":"TB Alton","year":"2014","unstructured":"Alton TB, Gee AO. Classifications in brief: Letournel classification for acetabular fractures. Clin Orthop Relat Res. 2014;472(1):35\u20138. https:\/\/doi.org\/10.1007\/s11999-013-3375-y.","journal-title":"Clin Orthop Relat Res"},{"issue":"2","key":"2753_CR29","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1148\/rg.352140098","volume":"35","author":"MH Scheinfeld","year":"2015","unstructured":"Scheinfeld MH, Dym AA, Spektor M, Avery LL, Dym RJ, Amanatullah DF. Acetabular fractures: what radiologists should know and how 3D CT can aid classification. Radiographics. 2015;35(2):555\u201377. https:\/\/doi.org\/10.1148\/rg.352140098.","journal-title":"Radiographics"},{"issue":"2","key":"2753_CR30","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.injury.2012.10.010","volume":"44","author":"G White","year":"2013","unstructured":"White G, Kanakaris NK, Faour O, Valverde JA, Martin MA, Giannoudis PV. Quadrilateral plate fractures of the acetabulum: an update. Injury. 2013;44(2):159\u201367. https:\/\/doi.org\/10.1016\/j.injury.2012.10.010.","journal-title":"Injury"},{"issue":"4","key":"2753_CR31","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/j.rcl.2019.02.004","volume":"57","author":"D Dreizin","year":"2019","unstructured":"Dreizin D, LeBedis CA, Nascone JW. Imaging acetabular fractures. Radiol Clin North Am. 2019;57(4):823\u201341. https:\/\/doi.org\/10.1016\/j.rcl.2019.02.004.","journal-title":"Radiol Clin North Am"},{"issue":"4","key":"2753_CR32","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1111\/j.1757-7861.2009.00053.x","volume":"1","author":"Q Zhang","year":"2009","unstructured":"Zhang Q, Chen W, Liu HJ, Li ZY, Song ZH, Pan JS, Zhang YZ. The role of the calcar femorale in stress distribution in the proximal femur. Orthop Surg. 2009;1(4):311\u20136. https:\/\/doi.org\/10.1111\/j.1757-7861.2009.00053.x.","journal-title":"Orthop Surg"},{"issue":"12","key":"2753_CR33","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1097\/00004424-199012000-00007","volume":"25","author":"RG Stiles","year":"1990","unstructured":"Stiles RG, Laverina CJ, Resnick D, Convery FR. The calcar femorale: an anatomic, radiologic, and surgical correlative study. Invest Radiol. 1990;25(12):1311\u20136. https:\/\/doi.org\/10.1097\/00004424-199012000-00007.","journal-title":"Invest Radiol"},{"issue":"9","key":"2753_CR34","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1097\/CORR.0000000000000861","volume":"477","author":"JM Rizkalla","year":"2019","unstructured":"Rizkalla JM, Lines T, Nimmons S. Classifications in brief: the Denis classification of sacral fractures. Clin Orthop Relat Res. 2019;477(9):2178\u201384. https:\/\/doi.org\/10.1097\/CORR.0000000000000861.","journal-title":"Clin Orthop Relat Res"},{"key":"2753_CR35","doi-asserted-by":"publisher","first-page":"18294","DOI":"10.1038\/s41598-021-97837-9","volume":"11","author":"AML Meesters","year":"2021","unstructured":"Meesters AML, ten Duis K, Kraeima J, Banierink H, Stirler VMA, Wouters PCR, de Vries JPPM, Witjes MJH, IJpma FFA. The accuracy of gap and step-off measurements in acetabular fracture treatment. Sci Rep. 2021;11:18294. https:\/\/doi.org\/10.1038\/s41598-021-97837-9.","journal-title":"Sci Rep"},{"issue":"1","key":"2753_CR36","doi-asserted-by":"publisher","first-page":"79","DOI":"10.4103\/0019-5413.58610","volume":"44","author":"RK Sen","year":"2010","unstructured":"Sen RK, Veerappa LA. Outcome analysis of pelvic ring fractures. Indian J Orthop. 2010;44(1):79\u201383. https:\/\/doi.org\/10.4103\/0019-5413.58610.","journal-title":"Indian J Orthop"},{"issue":"7","key":"2753_CR37","doi-asserted-by":"publisher","first-page":"2812","DOI":"10.1088\/1361-6560\/aa6055","volume":"62","author":"Y Fu","year":"2017","unstructured":"Fu Y, Liu S, Li HH, Yang D. Automatic and hierarchical segmentation of the human skeleton in CT images. Phys Med Biol. 2017;62(7):2812\u201333. https:\/\/doi.org\/10.1088\/1361-6560\/aa6055.","journal-title":"Phys Med Biol"},{"key":"2753_CR38","doi-asserted-by":"publisher","unstructured":"Singhal SK, Goswami B, Iwahori Y, Bhuyan MK, Ouchi A, Shimizu Y. Segmentation of bone tissue from CT images. In: Computer vision and machine intelligence: proceedings of CVMI 2022. Lecture notes in networks and systems. 2023;586. Springer. https:\/\/doi.org\/10.1007\/978-981-19-7867-8_19","DOI":"10.1007\/978-981-19-7867-8_19"},{"key":"2753_CR39","doi-asserted-by":"publisher","first-page":"100303","DOI":"10.1016\/j.ejro.2020.100303","volume":"8","author":"G Zeng","year":"2021","unstructured":"Zeng G, Schmaranzer F, Degonda C, Gerber N, Gerber K, Tannast M, Burger J, Siebenrock KA, Zheng G, Lerch TD. MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation. Eur J Radiol Open. 2021;8:100303. https:\/\/doi.org\/10.1016\/j.ejro.2020.100303.","journal-title":"Eur J Radiol Open"},{"key":"2753_CR40","doi-asserted-by":"publisher","first-page":"841202","DOI":"10.3389\/fmed.2022.841202","volume":"9","author":"X Chen","year":"2022","unstructured":"Chen X, Liu X, Wang Y, Ma R, Zhu S, Li S, Li S, Dong X, Li H, Wang G, Wu Y, Zhang Y, Qiu G, Qian W. Development and validation of an artificial intelligence preoperative planning system for total hip arthroplasty. Front Med (Lausanne). 2022;9:841202. https:\/\/doi.org\/10.3389\/fmed.2022.841202.","journal-title":"Front Med (Lausanne)"},{"issue":"3","key":"2753_CR41","doi-asserted-by":"publisher","first-page":"689","DOI":"10.3390\/jcm13030689","volume":"13","author":"J H\u00eaches","year":"2024","unstructured":"H\u00eaches J, Marcadent S, Fernandez A, Adjahou S, Meuwly JY, Thiran JP, Desseauve D, Favre J. Accuracy and reliability of pelvimetry measures obtained by manual or automatic labeling of three-dimensional pelvic models. J Clin Med. 2024;13(3):689. https:\/\/doi.org\/10.3390\/jcm13030689.","journal-title":"J Clin Med"},{"key":"2753_CR42","doi-asserted-by":"publisher","unstructured":"Shao J, Wu Q, Zhang Y, Liu C, Huo X, Wang C. Automatic 3D pelvimetry framework in CT images and its validation. Sci Rep. 2024;14(1). https:\/\/doi.org\/10.1038\/s41598-024-72123-6.","DOI":"10.1038\/s41598-024-72123-6"},{"issue":"7","key":"2753_CR43","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s11999-008-0632-6","volume":"467","author":"J Zwingmann","year":"2009","unstructured":"Zwingmann J, Konrad G, Kotter E, S\u00fcdkamp NP, Oberst M. Computer-navigated iliosacral screw insertion reduces malposition rate and radiation exposure. Clin Orthop Relat Res. 2009;467(7):1833\u20138. https:\/\/doi.org\/10.1007\/s11999-008-0632-6.","journal-title":"Clin Orthop Relat Res"},{"key":"2753_CR44","doi-asserted-by":"publisher","unstructured":"Yu H, Wang H, Shi Y, Xu K, Yu X, Cao Y. The segmentation of bones in pelvic CT images based on extraction of key frames. BMC Med Imaging. 2018;18(1). https:\/\/doi.org\/10.1186\/s12880-018-0260-x.","DOI":"10.1186\/s12880-018-0260-x"},{"issue":"11","key":"2753_CR45","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.1016\/j.patcog.2013.04.006","volume":"46","author":"Y Cheng","year":"2013","unstructured":"Cheng Y, Zhou S, Wang Y, Guo C, Bai J, Tamura S. Automatic segmentation technique for acetabulum and femoral head in CT images. Pattern Recognit. 2013;46(11):2969\u201384. https:\/\/doi.org\/10.1016\/j.patcog.2013.04.006.","journal-title":"Pattern Recognit"},{"issue":"3","key":"2753_CR46","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1007\/s11999-008-0682-9","volume":"467","author":"W K\u00f6hnlein","year":"2009","unstructured":"K\u00f6hnlein W, Ganz R, Impellizzeri FM, Leunig M. Acetabular morphology: implications for joint-preserving surgery. Clin Orthop Relat Res. 2009;467(3):682\u201391. https:\/\/doi.org\/10.1007\/s11999-008-0682-9.","journal-title":"Clin Orthop Relat Res"},{"issue":"5","key":"2753_CR47","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1109\/TMI.2025.3650126","volume":"45","author":"Y Sang","year":"2026","unstructured":"Sang Y, Liu Y, Yibulayimu S, et al. Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-Ray: Summary of the PENGWIN 2024 Challenge. IEEE Trans Med Imaging. 2026;45(5):2212\u201328. https:\/\/doi.org\/10.1109\/TMI.2025.3650126.","journal-title":"IEEE Trans Med Imaging"},{"key":"2753_CR48","doi-asserted-by":"publisher","first-page":"102412","DOI":"10.1016\/j.compmedimag.2024.102412","volume":"116","author":"B Zeng","year":"2024","unstructured":"Zeng B, Wang H, Joskowicz L, Chen X. Fragment distance-guided dual-stream learning for automatic pelvic fracture segmentation. Comput Med Imaging Graph. 2024;116:102412. https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102412.","journal-title":"Comput Med Imaging Graph"},{"issue":"7","key":"2753_CR49","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1002\/jor.26086","volume":"43","author":"D Hopkins","year":"2025","unstructured":"Hopkins D, Callary SA, Solomon LB, Lee PVS, Ackland DC. Automated Acetabular Defect Reconstruction and Analysis for Revision Total Hip Arthroplasty: A Computational Modeling Study. J Orthop Res. 2025;43(7):1315\u201324. https:\/\/doi.org\/10.1002\/jor.26086.","journal-title":"J Orthop Res"},{"key":"2753_CR50","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.cmpb.2015.12.012","volume":"127","author":"B Liu","year":"2016","unstructured":"Liu B, Zhang H, Hua S, Jiang Q, Huang R, Liu W, Zhang S, Zhang B, Yue Z. An automatic segmentation system of acetabulum in sequential CT images for the personalized artificial femoral head design. Comput Methods Programs Biomed. 2016;127:318\u201335. https:\/\/doi.org\/10.1016\/j.cmpb.2015.12.012.","journal-title":"Comput Methods Programs Biomed"},{"issue":"8","key":"2753_CR51","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1007\/s11548-020-02140-z","volume":"15","author":"MS Ead","year":"2020","unstructured":"Ead MS, Westover L, Polege S, McClelland S, Jaremko JL, Duke KK. Virtual reconstruction of unilateral pelvic fractures by using pelvic symmetry. Int J Comput Assist Radiol Surg. 2020;15(8):1267\u201377. https:\/\/doi.org\/10.1007\/s11548-020-02140-z.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"1","key":"2753_CR52","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1002\/hcs2.25","volume":"2","author":"Q Zheng","year":"2023","unstructured":"Zheng Q, Xu K, Zhan X, Huang F, Wang L, Chen S, Li J, Yang C, Chen Y, Fan S. Investigation of pelvic symmetry: a systematic analysis using computer aided design software. Health Care Sci. 2023;2(1):36\u201344. https:\/\/doi.org\/10.1002\/hcs2.25.","journal-title":"Health Care Sci"},{"key":"2753_CR53","doi-asserted-by":"publisher","unstructured":"Bakhshayesh P, Zaghloul A, Sephton BM, Enocson A. A novel 3D technique to assess symmetry of hemi pelvises. Sci Rep. 2020;10(1). https:\/\/doi.org\/10.1038\/s41598-020-75884-y.","DOI":"10.1038\/s41598-020-75884-y"},{"key":"2753_CR54","doi-asserted-by":"publisher","first-page":"103207","DOI":"10.1016\/j.media.2024.103207","volume":"95","author":"A Diaz-Pinto","year":"2024","unstructured":"Diaz-Pinto A, Alle S, Nath V, Tang Y, Ihsani A, Asad M, P\u00e9rez-Garc\u00eda F, Mehta P, Li W, Flores M, Roth HR, Vercauteren T, Xu D, Dogra P, Ourselin S, Feng A, Cardoso MJ. MONAI Label: a framework for AI-assisted interactive labeling of 3D medical images. Med Image Anal. 2024;95:103207. https:\/\/doi.org\/10.1016\/j.media.2024.103207.","journal-title":"Med Image Anal"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-026-02753-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02753-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02753-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T15:44:36Z","timestamp":1789573476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-026-02753-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2753"],"URL":"https:\/\/doi.org\/10.1186\/s12880-026-02753-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-10073453\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]},"assertion":[{"value":"18 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Ethical approval for this study was granted by the Institutional Review Board of the Directorate of Health Affairs, Sharqia Governorate, Ministry of Health and Population, Arab Republic of Egypt (IRB Registration Number: SCCREIRB-DIRCTRATHLTHAFFRSHRKYA-GOVAGNCY-001-290425-060), through an official \u201cAuthorization Approval for the Use of Anonymized CT Imaging Data\u201d dated 1 July 2025. All methods were carried out in accordance with relevant guidelines and regulations and with the principles of the Declaration of Helsinki. The requirement for individual informed consent to participate was waived by the same Institutional Review Board, as this was a retrospective study using fully anonymized imaging data; under Egyptian ethical regulations, separate individual consent was deemed unnecessary for this type of data, as stated in the authorization. All imaging data were fully de-identified and contained no names, national identification numbers, or direct personal identifiers, and were handled under conditions of secure storage, ethical use, and no patient re-identification.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent to Publish","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"454"}}