{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T17:26:26Z","timestamp":1772213186322,"version":"3.50.1"},"publisher-location":"Wiesbaden","reference-count":20,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"value":"9783658474218","type":"print"},{"value":"9783658474225","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-658-47422-5_72","type":"book-chapter","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T16:49:04Z","timestamp":1740847744000},"page":"311-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Category-fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images"],"prefix":"10.1007","author":[{"given":"Daiqi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuxin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"72_CR1","doi-asserted-by":"crossref","unstructured":"Kowal J, Langlotz F, Nolte LP. Basics of computer-assisted orthopaedic surgery. Navigation and MIS in Orthopedic Surgery. Springer, 2007:2\u20138.","DOI":"10.1007\/978-3-540-36691-1_1"},{"key":"72_CR2","doi-asserted-by":"crossref","unstructured":"Sugano N. Computer-assisted orthopaedic surgery and robotic surgery in total hip arthroplasty. Clin Orthop Surg. 2013;5(1):1\u20139.","DOI":"10.4055\/cios.2013.5.1.1"},{"key":"72_CR3","doi-asserted-by":"crossref","unstructured":"Liu J, Li H, Zeng B et al. An end-to-end geometry-based pipeline for automatic preoperative surgical planning of pelvic fracture reduction and fixation. IEEE Trans Med Imaging. 2024.","DOI":"10.1109\/TMI.2024.3429403"},{"key":"72_CR4","unstructured":"Wu J. Segmentation and fracture detection in CT images for traumatic pelvic injuries.Virginia Commonwealth University, Richmond, 2012."},{"key":"72_CR5","doi-asserted-by":"crossref","unstructured":"Pandey P, Guy P, Hodgson AJ et al. Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study. Int J Comput Assist Radiol Surg. 2018;13:1515\u201324.","DOI":"10.1007\/s11548-018-1788-5"},{"key":"72_CR6","doi-asserted-by":"crossref","unstructured":"Liu Y, Yibulayimu S, Sang Y et al. Pelvic fracture segmentation using a multi-scale distanceweighted neural network. Proc MICCAI. 2023:312\u201321.","DOI":"10.1007\/978-3-031-43996-4_30"},{"key":"72_CR7","doi-asserted-by":"crossref","unstructured":"Fornaro J, Sz\u00e9kely G, Harders M. Semi-automatic segmentation of fractured pelvic bones for surgical planning. Proc ISBMS. 2010:82\u20139.","DOI":"10.1007\/978-3-642-11615-5_9"},{"key":"72_CR8","doi-asserted-by":"crossref","unstructured":"Irwansyah, Lai JY, Essomba T et al. Algorithm for segmentation and reduction of fractured bones in computer-aided preoperative surgery. Proc ICBBE. 2016:12\u20138.","DOI":"10.1145\/3022702.3022703"},{"key":"72_CR9","doi-asserted-by":"crossref","unstructured":"Cernazanu-Glavan C, Holban S. Segmentation of bone structure in X-ray images using convolutional neural network. Adv Electr Comput Eng. 2013;13(1):87\u201394.","DOI":"10.4316\/AECE.2013.01015"},{"key":"72_CR10","doi-asserted-by":"crossref","unstructured":"Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med. 2018;98:8\u201315.","DOI":"10.1016\/j.compbiomed.2018.05.011"},{"key":"72_CR11","doi-asserted-by":"crossref","unstructured":"Ukai K, Rahman R,YagiNet al. Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Sci Rep. 2021;11(1):11716.","DOI":"10.1038\/s41598-021-91144-z"},{"key":"72_CR12","doi-asserted-by":"crossref","unstructured":"Yamamoto N, Rahman R, Yagi N et al. An automated fracture detection from pelvic CT images with 3-D convolutional neural networks. Proc IEEE CcS. 2020:1\u20136.","DOI":"10.1109\/CcS49175.2020.9231453"},{"key":"72_CR13","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y et al. Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. Proc MICCAI. 2021:272\u201384.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"72_CR14","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P et al. Mask r-CNN. Proc IEEE ICCV. 2017:2961\u20139.","DOI":"10.1109\/ICCV.2017.322"},{"key":"72_CR15","doi-asserted-by":"crossref","unstructured":"Unberath M, Zaech JN, Lee SC et al. DeepDRR \u2013 a catalyst for machine learning in fluoroscopy-guided procedures. Proc MICCAI. 2018:98\u2013106.","DOI":"10.1007\/978-3-030-00937-3_12"},{"key":"72_CR16","unstructured":"Isensee F, J\u00e4ger PF,Kohl SAet al.Automated design of deep learning methods for biomedical image segmentation. arXiv: 1904.08128. 2019."},{"key":"72_CR17","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Kohl SA et al. nnU-Net: a self-configuring method for deep learningbased biomedical image segmentation. Nat Methods. 2021;18(2):203\u201311.","DOI":"10.1038\/s41592-020-01008-z"},{"key":"72_CR18","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N et al. UNet++: a nested U-Net architecture for medical image segmentation. Proc DLMIA. 2018:3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"72_CR19","unstructured":"Oktay O, Schlemper J, Folgoc LL et al. Attention U-Net: learning where to look for the pancreas. arXiv: 1804.03999. 2018."},{"key":"72_CR20","doi-asserted-by":"crossref","unstructured":"Ruan J, Xiang S. VM-UNet: vision mamba UNet for medical image segmentation. arXiv: 2402.02491. 2024.","DOI":"10.1145\/3767748"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2025"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-47422-5_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:43:12Z","timestamp":1772210592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-47422-5_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783658474218","9783658474225"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-47422-5_72","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"value":"1431-472X","type":"print"},{"value":"2628-8958","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"BVM Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Regensburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 March 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 March 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bvm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bvm-conf.org\/de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}