{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:36:45Z","timestamp":1780342605312,"version":"3.54.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["Z01 CL040004"],"award-info":[{"award-number":["Z01 CL040004"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D off-the-shelf nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>For LNs with short axis diameter <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ge $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>\u2265<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 8\u00a0mm, the 3D cascade nnUNet model obtained the highest Dice score of 67.9 \u00b1 23.4 and lowest Hausdorff distance error of 22.8 \u00b1 20.2. For LNs of all sizes, the Dice score was 58.7 \u00b1 21.3 and this represented a <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ge $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>\u2265<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>10% improvement over a recently published approach evaluated on the same test dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has the potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-024-03165-4","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T18:01:55Z","timestamp":1715623315000},"page":"1537-1544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Segmentation of mediastinal lymph nodes in CT with anatomical priors"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8440-1702","authenticated-orcid":false,"given":"Tejas Sudharshan","family":"Mathai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bohan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"issue":"9","key":"3165_CR1","first-page":"1509","volume":"45","author":"M Torabi","year":"2004","unstructured":"Torabi M, Aquino SL, Harisinghani MG (2004) Current concepts in lymph node imaging. J Nucl Med 45(9):1509\u20131518","journal-title":"J Nucl Med"},{"issue":"1","key":"3165_CR2","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1102\/1470-7330.2009.0017","volume":"9","author":"S Ganeshalingam","year":"2009","unstructured":"Ganeshalingam S, Koh D-M (2009) Nodal staging. Cancer Imag 9(1):104\u201311","journal-title":"Cancer Imag"},{"key":"3165_CR3","doi-asserted-by":"crossref","unstructured":"Matthais T (2007) Imag Lymph Nodes - MRI and CT. Springer, pp 321\u2013329","DOI":"10.1007\/978-3-540-68212-7_15"},{"issue":"2","key":"3165_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3322\/caac.21388","volume":"67","author":"MB Amin","year":"2017","unstructured":"Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP (2017) The eighth edition ajcc cancer staging manual: Continuing to build a bridge from a population-based to a more personalized approach to cancer staging. CA Cancer J Clin 67(2):93\u201399","journal-title":"CA Cancer J Clin"},{"issue":"36","key":"3165_CR5","first-page":"1","volume":"2","author":"M Yun","year":"2014","unstructured":"Yun M, Sandeep H, Harisinghani Mukesh G (2014) Radiologic assessment of lymph nodes in oncologic patients. Curr Radiol Rep 2(36):1\u201313","journal-title":"Curr Radiol Rep"},{"key":"3165_CR6","first-page":"90350M","volume-title":"Medical imaging 2014: computer-aided diagnosis","author":"J Liu","year":"2014","unstructured":"Liu J, Zhao J, Hoffman J, Yao J, Zhang W, Turkbey EB, Wang S, Kim C, Summers RM (2014) Mediastinal lymph node detection on thoracic CT scans using spatial prior from multi-atlas label fusion. In: Aylward S, Hadjiiski LM (eds) Medical imaging 2014: computer-aided diagnosis, vol 9035. International Society for Optics and Photonics, SPIE, p 90350M"},{"key":"3165_CR7","first-page":"520","volume":"8673","author":"L Roth Holger","year":"2014","unstructured":"Roth Holger L, Le SA, Cherry Kevin M, Joanne H, Shijun W, Jiamin L, Evrim T, Summers Ronald M (2014) A new 2.5d representation for lymph node detection using random sets of deep convolutional neural network observations. Med Image Computing Comput-Assisted Interv- MICCAI 2014 8673:520\u2013527","journal-title":"Med Image Computing Comput-Assisted Interv- MICCAI 2014"},{"key":"3165_CR8","first-page":"1057502","volume-title":"Medical imaging 2018: computer-aided diagnosis","author":"H Oda","year":"2018","unstructured":"Oda H, Roth HR, Bhatia KK, Oda M, Kitasaka T, Iwano S, Homma H, Takabatake H, Mori M, Natori H, Schnabel JA, Mori K (2018) Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images. In: Petrick N, Mori K (eds) Medical imaging 2018: computer-aided diagnosis, vol 10575. International Society for Optics and Photonics, SPIE, p 1057502"},{"issue":"6","key":"3165_CR9","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1007\/s11548-019-01948-8","volume":"14","author":"D Bouget","year":"2019","unstructured":"Bouget D, J\u00f8rgensen A, Kiss G, Leira HO, Lang\u00f8 T (2019) Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in ct data for lung cancer staging. Int J Comput Assist Radiol Surg 14(6):977\u2013986","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3165_CR10","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s12880-021-00599-z","volume":"21","author":"A-I Iuga","year":"2021","unstructured":"Iuga A-I, Carolus H, H\u00f6ink AJ, Brosch T, Klinder T, Maintz D, Persigehl T, Bae\u00dfler B, P\u00fcsken M (2021) Automated detection and segmentation of thoracic lymph nodes from ct using 3d foveal fully convolutional neural networks. BMC Med Imaging 21:69","journal-title":"BMC Med Imaging"},{"key":"3165_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.109718","volume":"139","author":"A-I Iuga","year":"2021","unstructured":"Iuga A-I, Lossau T, Caldeira LL, Rinneburger M, Lennartz S, Hokamp NG, P\u00fcsken M, Carolus H, Maintz D, Klinder T, Persigehl T (2021) Automated mapping and n-staging of thoracic lymph nodes in contrast-enhanced ct scans of the chest using a fully convolutional neural network. Eur J Radiol 139:109718","journal-title":"Eur J Radiol"},{"issue":"1","key":"3165_CR12","first-page":"44","volume":"11","author":"D Bouget","year":"2023","unstructured":"Bouget D, Pedersen A, Vanel J, Leira HO, Lang\u00f8 T (2023) Mediastinal lymph nodes segmentation using 3d convolutional neural network ensembles and anatomical priors guiding. Comput Methods Biomech Biomed Eng: Imag Vis 11(1):44\u201358","journal-title":"Comput Methods Biomech Biomed Eng: Imag Vis"},{"key":"3165_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102312","volume":"111","author":"A Mehrtash","year":"2024","unstructured":"Mehrtash A, Ziegler E, Idris T, Somarouthu B, Urban T, LaCasce AS, Jacene H, Van Den Abbeele AD, Pieper S, Harris G, Kikinis R, Kapur T (2024) Evaluation of mediastinal lymph node segmentation of heterogeneous ct data with full and weak supervision. Comput Med Imaging Graph 111:102312","journal-title":"Comput Med Imaging Graph"},{"issue":"17","key":"3165_CR14","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.1158\/0008-5472.CAN-18-0494","volume":"78","author":"Y Lu","year":"2018","unstructured":"Lu Y, Yu Q, Gao Y, Zhou Y, Liu G, Dong Q, Ma J, Ding L, Yao H, Zhang Z, Xiao G, An Q, Wang G, Xi J, Yuan W-T, Lian Y, Zhang D, Zhao C-G, Yao Q, Liu W, Zhou X, Liu S, Wu Q, Xu W, Zhang J, Wang D, Sun Z, Gao Y, Zhang X, Hu J, Zhang M, Wang G, Zheng X, Wang L, Zhao J, Yang S (2018) Identification of metastatic lymph nodes in mr imaging with faster region-based convolutional neural networks. Can Res 78(17):5135\u20135143","journal-title":"Can Res"},{"key":"3165_CR15","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.8052","volume":"7","author":"A Debats Oscar","year":"2019","unstructured":"Debats Oscar A, Litjens Geert JS, Huisman Henkjan J (2019) Lymph node detection in mr lymphography: false positive reduction using multi-view convolutional neural networks. PeerJ 7:e8052","journal-title":"PeerJ"},{"key":"3165_CR16","doi-asserted-by":"crossref","unstructured":"Mathai TS, Lee S, Elton DC, Shen TC, Peng Y, Lu Z, Summers RM (2021) Detection of lymph nodes in T2 MRI using neural network ensembles. In: Lian C, Cao X, Rekik I, Xuanang X, Yan P (eds) Machine learning in medical imaging. Springer International Publishing, Cham, pp 682\u2013691","DOI":"10.1007\/978-3-030-87589-3_70"},{"key":"3165_CR17","first-page":"120333B","volume-title":"Medical imaging 2022: computer-aided diagnosis","author":"TS Mathai","year":"2022","unstructured":"Mathai TS, Lee S, Elton DC, Shen TC, Peng Y, Zhiyong L, Summers RM (2022) Lymph node detection in T2 MRI with transformers. In: Karen D, Iftekharuddin Khan M (eds) Medical imaging 2022: computer-aided diagnosis, vol 12033. International Society for Optics and Photonics, SPIE, p 120333B"},{"key":"3165_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102345","volume":"77","author":"S Wang","year":"2022","unstructured":"Wang S, Zhu Y, Lee S, Elton DC, Shen TC, Tang Y, Peng Y, Lu Z, Summers RM (2022) Global-local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images. Med Image Anal 77:102345","journal-title":"Med Image Anal"},{"issue":"2","key":"3165_CR19","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s11548-022-02782-1","volume":"18","author":"TS Mathai","year":"2022","unstructured":"Mathai TS, Lee S, Shen TC, Lu Z, Summers RM (2022) Universal lymph node detection in T2 MRI using neural networks. Int J CARS 18(2):313\u2013318","journal-title":"Int J CARS"},{"issue":"1","key":"3165_CR20","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s11548-023-02954-7","volume":"19","author":"TS Mathai","year":"2023","unstructured":"Mathai TS, Lee S, Shen TC, Elton D, Lu Z, Summers RM (2023) Universal detection and segmentation of lymph nodes in multi-parametric MRI. Int J Comput Assist Radiol Surg 19(1):163\u2013170","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"6","key":"3165_CR21","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"KW Clark","year":"2013","unstructured":"Clark KW, Vendt BA, Smith KE, Freymann JB, Kirby JS, Koppel P, Moore SM, Phillips SR, Maffitt DR, Pringle M, Tarbox L, Prior FW (2013) The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digital Imaging 26(6):1045\u20131057","journal-title":"J. Digital Imaging"},{"key":"3165_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102363","volume":"114","author":"TS Mathai","year":"2024","unstructured":"Mathai TS, Shen TC, Elton DC, Lee S, Zhiyong L, Summers RM (2024) Detection of abdominopelvic lymph nodes in multi-parametric MRI. Comput Med Imaging Graph 114:102363","journal-title":"Comput Med Imaging Graph"},{"issue":"12","key":"3165_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0144282","volume":"10","author":"PJ Reynisson","year":"2015","unstructured":"Reynisson PJ, Scali M, Smistad E, Hofstad EF, Leira HO, Lindseth F, Nagelhus Hernes TA, Amundsen T, Sorger H, Lang\u00f8 T (2015) Airway segmentation and centerline extraction from thoracic CT - comparison of a new method to state of the art commercialized methods. PLoS ONE 10(12):1-20","journal-title":"PLoS ONE"},{"issue":"5","key":"3165_CR24","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 (2023) Totalsegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiol: Artif Intell 5(5):230024","journal-title":"Radiol: Artif Intell"},{"key":"3165_CR25","doi-asserted-by":"crossref","unstructured":"Hou B, Mathai TS, Liu J, Parnell C, Summers RM (2024) Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study","DOI":"10.1007\/s11548-024-03167-2"},{"issue":"3","key":"3165_CR26","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s00261-023-04135-1","volume":"49","author":"MH Lee","year":"2023","unstructured":"Lee MH, Liu D, Garrett JW, Perez A, Zea R, Summers RM, Pickhardt PJ (2023) Comparing fully automated AI body composition measures derived from thin and thick slice CT image data. Abdom Radiol 49(3):985\u2013996","journal-title":"Abdom Radiol"},{"key":"3165_CR27","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203\u2013211","DOI":"10.1038\/s41592-020-01008-z"},{"key":"3165_CR28","doi-asserted-by":"crossref","unstructured":"Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, van Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, McHugo MK, Napel S, Golia Pernicka JS, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin JA, Ourselin S, Wiesenfarth M, Arbel\u00e1ez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Jorge Cardoso M (2022) The medical segmentation decathlon. Nat Commun 13(1):4128","DOI":"10.1038\/s41467-022-30695-9"},{"issue":"1","key":"3165_CR29","first-page":"31","volume":"8","author":"G Lehmann","year":"2007","unstructured":"Lehmann G (2007) Label object representation and manipulation with ITK. The Insight J 8(1):31","journal-title":"The Insight J"},{"key":"3165_CR30","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"H-Y Zhou","year":"2023","unstructured":"Zhou H-Y, Guo J, Zhang Y, Han X, Lequan Yu, Wang L, Yizhou Yu (2023) nnformer: Volumetric medical image segmentation via a 3d transformer. IEEE Trans Image Process 32:4036\u20134045","journal-title":"IEEE Trans Image Process"},{"key":"3165_CR31","unstructured":"Guanghui FU, Nichelli L, Herran D, Valabregue R, Alentorn A, Hoang-Xuan K, Houillier C, Dormont D, Leh\u00e9ricy S, Colliot O (2024) Comparing foundation models and nnu-net for segmentation of primary brain lymphoma on clinical routine post-contrast t1-weighted MRI. In: Submitted to Medical Imaging with Deep Learning. Under review"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03165-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03165-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03165-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T15:21:36Z","timestamp":1723821696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03165-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["3165"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03165-4","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"11 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"RMS receives royalties from iCAD, Philips, PingAn, ScanMed, and Translation Holdings. His lab received research support from PingAn. The authors have no additional Conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this study, informed consent was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}