{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T17:26:47Z","timestamp":1772213207662,"version":"3.50.1"},"publisher-location":"Wiesbaden","reference-count":16,"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T00:00:00Z","timestamp":1740873600000},"content-version":"vor","delay-in-days":60,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate segmentation of structures in CT is essential for clinical tasks such as tumour staging, radiotherapy planning, fracture assessment, and monitoring of disease progression. Current deep learning-based automated \"segmentators\" face challenges due to variability in scanner parameters, anatomical regions, and training data, which impact performance consistency across diverse datasets. We evaluated various total body segmentators on publicly available lung CT data excluded from their training sets. We found that these segmentators exhibit label mixing within individual ribs and vertebrae, often requiring anatomy-informed post-processing steps to improve accuracy. Combining multiple models and incorporating anatomical information enhances segmentation outcomes compared to using single models, highlighting the complementary strengths of different segmentation approaches and task-dependent a priori knowledge.<\/jats:p>","DOI":"10.1007\/978-3-658-47422-5_76","type":"book-chapter","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T16:47:17Z","timestamp":1740847637000},"page":"330-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Multiple Total Body Segmentators and Anatomy-informed Post-processing for Segmenting Bones in Lung CTs"],"prefix":"10.1007","author":[{"given":"Lukas","family":"F\u00f6rner","sequence":"first","affiliation":[]},{"given":"Kartikay","family":"Tehlan","sequence":"additional","affiliation":[]},{"given":"Constantin","family":"Bauer","sequence":"additional","affiliation":[]},{"given":"Josua A.","family":"Decker","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Wendler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"76_CR1","doi-asserted-by":"crossref","unstructured":"Mansoor A, Bagci U, Foster B et al. 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MOOSE vs TotalSegmentator: comparison of feature values of segmented anatomical regions in [18F] FDG PET\/CT images. Soc Nuclear Med. 2024."},{"key":"76_CR9","unstructured":"Hering A, Murphy K, Ginneken B van. Learn2Reg challenge: CT lung registration training data. 2020."},{"key":"76_CR10","doi-asserted-by":"crossref","unstructured":"Wasserthal J, Breit HC, Meyer MT et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol: Artif Intell. 2023;5(5):e230024.","DOI":"10.1148\/ryai.230024"},{"key":"76_CR11","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Kohl SAA 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":"76_CR12","doi-asserted-by":"crossref","unstructured":"Sundar LKS, Yu J, Muzik O et al. Fully automated, semantic segmentation of whole-body 18 F-FDG PET\/CT images based on data-centric artificial intelligence. Eur J Nucl Med. 2022;63(12):1941\u20138.","DOI":"10.2967\/jnumed.122.264063"},{"key":"76_CR13","unstructured":"Jaus A, Seibold C, Hermann K et al. Towards unifying anatomy segmentation: automated generation of a full-body CT dataset via knowledge aggregation and anatomical guidelines. arXiv: 2307.13375. 2023."},{"key":"76_CR14","doi-asserted-by":"crossref","unstructured":"Payer C, Stern D, Bischof H et al. Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net. Proc VISIGRAPP VISAPP. 2020:124\u201333.","DOI":"10.5220\/0008975201240133"},{"key":"76_CR15","doi-asserted-by":"crossref","unstructured":"L\u00f6ffler MT, Sekuboyina A, Jacob A et al. A vertebral segmentation dataset with fracture grading. 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