{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T20:38:14Z","timestamp":1788467894892,"version":"build-2803163510"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100021856","name":"Ministero dell\u2019 Universit\u00e0 e della Ricerca","doi-asserted-by":"crossref","award":["B53C22006170001"],"award-info":[{"award-number":["B53C22006170001"]}],"id":[{"id":"10.13039\/501100021856","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-07257-2","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T19:21:34Z","timestamp":1751397694000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI"],"prefix":"10.1038","volume":"15","author":[{"given":"Annachiara","family":"Cariola","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Sibilano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Guerriero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vitoantonio","family":"Bevilacqua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Brunetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"issue":"7","key":"7257_CR1","doi-asserted-by":"publisher","first-page":"928","DOI":"10.3174\/ajnr.A7462","volume":"43","author":"A Osborn","year":"2022","unstructured":"Osborn, A., Louis, D., Poussaint, T., Linscott, L. & Salzman, K. The 2021 world health organization classification of tumors of the central nervous system: what neuroradiologists need to know. Am. J. Neuroradiol. 43(7), 928\u2013937 (2022).","journal-title":"Am. J. Neuroradiol."},{"key":"7257_CR2","doi-asserted-by":"crossref","unstructured":"AlRayahi, J., Alwalid, O., Mubarak, W., Maaz, A.U.R., & Mifsud, W. Pediatric brain tumors in the molecular era: Updates for the radiologist. In Seminars in Roentgenology. Vol. 58. 47\u201366 (Elsevier, 2023).","DOI":"10.1053\/j.ro.2022.09.004"},{"key":"7257_CR3","doi-asserted-by":"publisher","first-page":"102006","DOI":"10.1016\/j.canep.2021.102006","volume":"74","author":"N Raja","year":"2021","unstructured":"Raja, N., Hayes, L., Basta, N. & McNally, R. J. International trends in the incidence of brain tumours in children and young-adults and their association with indicators of economic development. Cancer Epidemiol. 74, 102006 (2021).","journal-title":"Cancer Epidemiol."},{"issue":"6","key":"7257_CR4","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/S1470-2045(20)30173-X","volume":"21","author":"C Erker","year":"2020","unstructured":"Erker, C. et al. Response assessment in paediatric high-grade glioma: Recommendations from the response assessment in pediatric neuro-oncology (rapno) working group. Lancet Oncol. 21(6), 317\u2013329. https:\/\/doi.org\/10.1016\/S1470-2045(20)30173-X (2020).","journal-title":"Lancet Oncol."},{"key":"7257_CR5","doi-asserted-by":"publisher","unstructured":"Lutz, K., J\u00fcnger, S.T., & Messing-J\u00fcnger, M. Essential management of pediatric brain tumors. Children 9(4) (2022). https:\/\/doi.org\/10.3390\/children9040498.","DOI":"10.3390\/children9040498"},{"key":"7257_CR6","doi-asserted-by":"publisher","unstructured":"Magadza, T., & Viriri, S. Deep learning for brain tumor segmentation: A survey of state-of-the-art. J. Imaging 7, 19 (2021) https:\/\/doi.org\/10.3390\/jimaging7020019.","DOI":"10.3390\/jimaging7020019"},{"issue":"5","key":"7257_CR7","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/S1470-2045(22)00063-8","volume":"23","author":"G Morana","year":"2022","unstructured":"Morana, G. et al. Imaging response assessment for cns germ cell tumours: Consensus recommendations from the European Society for Paediatric Oncology Brain Tumour Group and North American Children\u2019s Oncology Group. Lancet Oncol. 23(5), 218\u2013228 (2022).","journal-title":"Lancet Oncol."},{"key":"7257_CR8","doi-asserted-by":"crossref","unstructured":"Avula, S., Peet, A., Morana, G., Morgan, P., Warmuth-Metz, M., Jaspan, T., & Paediatric Oncology (SIOPE)-Brain Tumour Imaging Group, E.S. European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours. Child\u2019s Nerv. Syst. 37(8), 2497\u20132508 (2021).","DOI":"10.1007\/s00381-021-05199-4"},{"key":"7257_CR9","doi-asserted-by":"publisher","first-page":"106405","DOI":"10.1016\/j.compbiomed.2022.106405","volume":"152","author":"R Ranjbarzadeh","year":"2023","unstructured":"Ranjbarzadeh, R., Caputo, A., Tirkolaee, E. B., Ghoushchi, S. J. & Bendechache, M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput. Biol. Med. 152, 106405 (2023).","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"7257_CR10","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1002\/jmri.28740","volume":"58","author":"YW Park","year":"2023","unstructured":"Park, Y. W. et al. The 2021 who classification for gliomas and implications on imaging diagnosis: Part 2\u2014Summary of imaging findings on pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. J. Magnet. Resonan. Imaging 58(3), 690\u2013708 (2023).","journal-title":"J. Magnet. Resonan. Imaging"},{"key":"7257_CR11","doi-asserted-by":"crossref","unstructured":"Ghosh, A., & Thakur, S. Review of brain tumor MRI image segmentation methods for brats challenge dataset. In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 405\u2013410 (IEEE, 2022).","DOI":"10.1109\/Confluence52989.2022.9734134"},{"key":"7257_CR12","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.mri.2019.05.043","volume":"61","author":"A Wadhwa","year":"2019","unstructured":"Wadhwa, A., Bhardwaj, A. & Verma, V. S. A review on brain tumor segmentation of MRI images. Magnet. Resonan. Imaging 61, 247\u2013259 (2019).","journal-title":"Magnet. Resonan. Imaging"},{"key":"7257_CR13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/RBME.2019.2946868","volume":"13","author":"M Ghaffari","year":"2019","unstructured":"Ghaffari, M., Sowmya, A. & Oliver, R. Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the brats 2012\u20132018 challenges. IEEE Rev. Biomed. Eng. 13, 156\u2013168 (2019).","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"7257_CR14","doi-asserted-by":"publisher","first-page":"1392807","DOI":"10.3389\/fbioe.2024.1392807","volume":"12","author":"ZU Abidin","year":"2024","unstructured":"Abidin, Z. U. et al. Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: A prospective survey. Front. Bioeng. Biotechnol. 12, 1392807 (2024).","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"1","key":"7257_CR15","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1177\/08465371231184780","volume":"75","author":"P Vafaeikia","year":"2024","unstructured":"Vafaeikia, P. et al. MRI-based end-to-end pediatric low-grade glioma segmentation and classification. Can. Assoc. Radiol. J. 75(1), 153\u2013160 (2024).","journal-title":"Can. Assoc. Radiol. J."},{"key":"7257_CR16","doi-asserted-by":"publisher","first-page":"713681","DOI":"10.3389\/fradi.2021.713681","volume":"1","author":"D Martin","year":"2021","unstructured":"Martin, D., Tong, E., Kelly, B., Yeom, K. & Yedavalli, V. Current perspectives of artificial intelligence in pediatric neuroradiology: An overview. Front. Radiol. 1, 713681 (2021).","journal-title":"Front. Radiol."},{"issue":"1","key":"7257_CR17","first-page":"027","volume":"5","author":"A Fathi Kazerooni","year":"2023","unstructured":"Fathi Kazerooni, A. et al. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study. Neuro-Oncol. Adv. 5(1), 027 (2023).","journal-title":"Neuro-Oncol. Adv."},{"key":"7257_CR18","doi-asserted-by":"publisher","unstructured":"Bengtsson, M., Keles, E., Durak, G., Anwar, S., Velichko, Y., Linguraru, M.G., & Waanders, A. A new logic for pediatric brain tumor segmentation. https:\/\/doi.org\/10.48550\/arXiv.2411.01390.","DOI":"10.48550\/arXiv.2411.01390"},{"key":"7257_CR19","doi-asserted-by":"publisher","unstructured":"Shaari, H., Kevri\u0107, J., Juki\u0107, S., Be\u0161i\u0107, L., Joki\u0107, D., Ahmed, N., & Rajs, V. Deep learning-based studies on pediatric brain tumors imaging: Narrative review of techniques and challenges. Brain Sci. 11(6) (2021). https:\/\/doi.org\/10.3390\/brainsci11060716.","DOI":"10.3390\/brainsci11060716"},{"key":"7257_CR20","doi-asserted-by":"publisher","unstructured":"Familiar, A. M. et al. Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation. Neuro-Oncology 26(9), 1557\u20131571. (2024). https:\/\/doi.org\/10.1093\/neuonc\/noae093. https:\/\/academic.oup.com\/neuro-oncology\/article-pdf\/26\/9\/1557\/59028626\/noae093.pdf.","DOI":"10.1093\/neuonc\/noae093"},{"key":"7257_CR21","unstructured":"Kazerooni, A.F. et al. The brain tumor segmentation in pediatrics (brats-peds) challenge: Focus on pediatrics (cbtn-connect-dipgr-asnr-miccai brats-peds). arXiv preprint arXiv:2404.15009 (2024)."},{"key":"7257_CR22","doi-asserted-by":"publisher","unstructured":"Fathi Kazerooni, A. et al. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study. Neuro-Oncol. Adv. 5(1), 027 (2023). https:\/\/doi.org\/10.1093\/noajnl\/vdad027. https:\/\/academic.oup.com\/noa\/article-pdf\/5\/1\/vdad027\/49806383\/vdad027.pdf.","DOI":"10.1093\/noajnl\/vdad027"},{"issue":"10","key":"7257_CR23","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7257_CR24","doi-asserted-by":"crossref","unstructured":"Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A.V., Criminisi, A., Rueckert, D., & Glocker, B. Deepmedic for brain tumor segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 2. 138\u2013149 (Springer, 2016).","DOI":"10.1007\/978-3-319-55524-9_14"},{"key":"7257_CR25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. 234\u2013241 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7257_CR26","doi-asserted-by":"publisher","first-page":"810","DOI":"10.3389\/fnins.2019.00810","volume":"13","author":"L Sun","year":"2019","unstructured":"Sun, L., Zhang, S., Chen, H. & Luo, L. Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13, 810. https:\/\/doi.org\/10.3389\/fnins.2019.00810 (2019).","journal-title":"Front. Neurosci."},{"key":"7257_CR27","doi-asserted-by":"crossref","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K.H. Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge (2018). arXiv:abs\/1802.10508.","DOI":"10.1007\/978-3-319-75238-9_25"},{"key":"7257_CR28","doi-asserted-by":"crossref","unstructured":"Henry, T., Carr\u00e9, A., Lerousseau, M., Estienne, T., Robert, C., Paragios, N., & Deutsch, E. Brain tumor segmentation with self-ensembled, deeply-supervised 3D u-net neural networks: a brats 2020 challenge solution. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I . Vol. 6. 327\u2013339 (Springer, 2021) .","DOI":"10.1007\/978-3-030-72084-1_30"},{"key":"7257_CR29","doi-asserted-by":"publisher","first-page":"105191","DOI":"10.1016\/j.imavis.2024.105191","volume":"149","author":"J Sachdeva","year":"2024","unstructured":"Sachdeva, J., Sharma, D. & Ahuja, C. K. Multiscale segmentation net for segregating heterogeneous brain tumors: Gliomas on multimodal MR images. Image Vis. Comput. 149, 105191 (2024).","journal-title":"Image Vis. Comput."},{"key":"7257_CR30","doi-asserted-by":"crossref","unstructured":"Ahuja, S., Panigrahi, B. & Gandhi, T.K. Fully automatic brain tumor segmentation using deeplabv3+ with variable loss functions. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). 522\u2013526 (IEEE, 2021).","DOI":"10.1109\/SPIN52536.2021.9566128"},{"issue":"2","key":"7257_CR31","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. H. nnu-net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021).","journal-title":"Nat. Methods"},{"key":"7257_CR32","unstructured":"Capell\u00e1n-Mart\u00edn, D. et al. Model ensemble for brain tumor segmentation in magnetic resonance imaging. arXiv preprint arXiv:2409.08232 (2024)."},{"key":"7257_CR33","doi-asserted-by":"crossref","unstructured":"Kharraji, M. et al. Brain tumor segmentation with advanced nnu-net: Pediatrics and adults tumors. In Brain Tumor Segmentation with Advanced nnU-Net: Pediatrics and Adults Tumors (Mostafa, S. et al. Eds.) (2023).","DOI":"10.2139\/ssrn.4514619"},{"key":"7257_CR34","unstructured":"Bengtsson, M. et al. A new logic for pediatric brain tumor segmentation. arXiv preprint arXiv:2411.01390 (2024)."},{"key":"7257_CR35","doi-asserted-by":"crossref","unstructured":"Vossough, A. et al. Training and comparison of nnu-net and deepmedic methods for autosegmentation of pediatric brain tumors. Am. J. Neuroradiol. (2024).","DOI":"10.3174\/ajnr.A8293"},{"key":"7257_CR36","doi-asserted-by":"publisher","unstructured":"Mahajan, A. et al. Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction. Explor. Target. Anti-tumor Ther. 4(4), 669\u2013684 (2023). https:\/\/doi.org\/10.37349\/etat.2023.00159.","DOI":"10.37349\/etat.2023.00159"},{"key":"7257_CR37","unstructured":"Hashmi, S. et al. Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics (2024). arXiv:abs\/2411.15872."},{"key":"7257_CR38","doi-asserted-by":"publisher","unstructured":"Mulvany, T., Griffiths-King, D., Novak, J., & Rose, H. Segmentation of Pediatric Brain Tumors Using a Radiologically Informed, Deep Learning Cascade. https:\/\/doi.org\/10.48550\/arXiv.2410.14020.","DOI":"10.48550\/arXiv.2410.14020"},{"key":"7257_CR39","doi-asserted-by":"publisher","unstructured":"Griffiths-King, D., Mulvany, T., Rose, H., & Novak, J. Deep-Learning Segmentation of Pediatric Brain Tumors Using Ratio Maps of T1w\/T2w MRI Signal Intensity. https:\/\/doi.org\/10.1101\/2025.04.09.25325530.","DOI":"10.1101\/2025.04.09.25325530"},{"key":"7257_CR40","unstructured":"Cardoso, M.J. et al. MONAI: An Open-Source Framework for Deep Learning in Healthcare (2022). https:\/\/arxiv.org\/abs\/2211.02701."},{"issue":"6","key":"7257_CR41","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.3390\/s25061838","volume":"25","author":"B Bonato","year":"2025","unstructured":"Bonato, B., Nanni, L. & Bertoldo, A. Advancing precision: A comprehensive review of MRI segmentation datasets from brats challenges (2012\u20132025). Sensors (Basel, Switzerland) 25(6), 1838 (2025).","journal-title":"Sensors (Basel, Switzerland)"},{"key":"7257_CR42","doi-asserted-by":"crossref","unstructured":"Myronenko, A. 3D MRI brain tumor segmentation using autoencoder regularization. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II. Vol. 4. 311\u2013320 (Springer, 2019).","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"7257_CR43","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7257_CR44","unstructured":"Kingma, D.P., & Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"7257_CR45","unstructured":"Loshchilov, I., & Hutter, F. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"7257_CR46","unstructured":"Kazerooni, A.F. et al. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023 (2024). arXiv:abs\/2407.08855."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07257-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07257-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07257-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T19:21:38Z","timestamp":1751397698000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07257-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["7257"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-07257-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"27 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22595"}}