{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T19:23:40Z","timestamp":1771529020366,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100018705","name":"HORIZON EUROPE European Institute of Innovation and Technology","doi-asserted-by":"publisher","award":["826494"],"award-info":[{"award-number":["826494"]}],"id":[{"id":"10.13039\/100018705","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01557-9","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:52:44Z","timestamp":1748346764000},"page":"411-421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9374-1411","authenticated-orcid":false,"given":"Mat\u00edas","family":"Fern\u00e1ndez-Pat\u00f3n","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Montoya-Filardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adri\u00e1n","family":"Galiana-Bordera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro Miguel","family":"Mart\u00ednez-Giron\u00e9s","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Veiga-Canuto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Blanca","family":"Mart\u00ednez de las Heras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonor","family":"Cerd\u00e1-Alberich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis","family":"Mart\u00ed-Bonmat\u00ed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"1557_CR1","doi-asserted-by":"publisher","unstructured":"Jovanovich, N., Habib, A., Head, J., Hameed, F., Agnihotri, S. and Zinn, P.O.: Pediatric diffuse midline glioma: Understanding the mechanisms and assessing the next generation of personalized therapeutics. Neuro-Oncology Advances,. https:\/\/doi.org\/10.1093\/noajnl\/vdad040, 2023.","DOI":"10.1093\/noajnl\/vdad040"},{"key":"1557_CR2","doi-asserted-by":"crossref","unstructured":"Louis, D.N., Perry, A., Reifenberger, G., Von Deimling, A., Figarella-Branger, D., Cavenee, W.K. et al.: The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica, : Springer. 131, 803\u201320. , 2016.","DOI":"10.1007\/s00401-016-1545-1"},{"key":"1557_CR3","doi-asserted-by":"publisher","unstructured":"Vanan, M.I. and Eisenstat, D.D.: DIPG in children - What can we learn from the past? Frontiers in Oncology, 5. https:\/\/doi.org\/10.3389\/fonc.2015.00237, 2015.","DOI":"10.3389\/fonc.2015.00237"},{"key":"1557_CR4","doi-asserted-by":"publisher","unstructured":"Chegraoui, H., Philippe, C., Dangouloff-Ros, V., Grigis, A., Calmon, R., Boddaert, N. et al.: Object detection improves tumour segmentation in mr images of rare brain tumours. Cancers, 13, 1\u201319. https:\/\/doi.org\/10.3390\/cancers13236113, 2021.","DOI":"10.3390\/cancers13236113"},{"key":"1557_CR5","doi-asserted-by":"publisher","unstructured":"Huang, R.Y. and Wen, P.Y.: Response Assessment in Neuro-Oncology Criteria and Clinical Endpoints. Magnetic Resonance Imaging Clinics of North America, : Elsevier Inc. 24, 705\u201318. https:\/\/doi.org\/10.1016\/j.mric.2016.06.003, 2016.","DOI":"10.1016\/j.mric.2016.06.003"},{"key":"1557_CR6","unstructured":"Avval, A.H., Banerjee, S., Zielke, J., Kann, B.H., Mueller, S. and Rauschecker, A.M.: Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma. 1\u201315. , 2025."},{"key":"1557_CR7","doi-asserted-by":"publisher","unstructured":"Wagner, M.W., Namdar, K., Napoleone, M., Hainc, N., Amirabadi, A., Fonseca, A. et al.: Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma\/Diffuse Intrinsic Pontine Glioma. Canadian Association of Radiologists Journal,. https:\/\/doi.org\/10.1177\/08465371221109921, 2023.","DOI":"10.1177\/08465371221109921"},{"key":"1557_CR8","doi-asserted-by":"publisher","unstructured":"Tam, L.T., Yeom, K.W., Wright, J.N., Jaju, A., Radmanesh, A., Han, M. et al.: MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: An international study. Neuro-Oncology Advances, 3, 1\u20139. https:\/\/doi.org\/10.1093\/noajnl\/vdab042, 2021.","DOI":"10.1093\/noajnl\/vdab042"},{"key":"1557_CR9","doi-asserted-by":"publisher","unstructured":"Goya-Outi, J., Calmon, R., Orlhac, F., Philippe, C., Boddaert, N., Puget, S. et al.: Can structural MRI radiomics predict DIPG histone H3 mutation and patient overall survival at diagnosis time? 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings,. https:\/\/doi.org\/10.1109\/BHI.2019.8834524, 2019.","DOI":"10.1109\/BHI.2019.8834524"},{"key":"1557_CR10","doi-asserted-by":"publisher","unstructured":"Tinkle, C., Hsu, C.-Y., Simpson, E., Chiang, J., Li, X., Armstrong, J. et al.: NIMG-51. CONVENTIONAL MRI RADIOMIC FEATURES IMPROVE PROGNOSTICATION AND ARE PREDICTIVE OF H3 K27M STATUS IN DIPG. Neuro-Oncology,. https:\/\/doi.org\/10.1093\/neuonc\/noaa215.664, 2020.","DOI":"10.1093\/neuonc\/noaa215.664"},{"key":"1557_CR11","doi-asserted-by":"publisher","unstructured":"I\u015fin, A., Direko\u01e7lu, C. and \u015eah, M.: Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods. Procedia Computer Science, 102, 317\u201324. https:\/\/doi.org\/10.1016\/j.procs.2016.09.407, 2016.","DOI":"10.1016\/j.procs.2016.09.407"},{"key":"1557_CR12","doi-asserted-by":"publisher","unstructured":"Munir, K., Frezza, F. and Rizzi, A.: Deep learning for brain tumor segmentation. Studies in Computational Intelligence, 908, 189\u2013201. https:\/\/doi.org\/10.1007\/978-981-15-6321-8_11, 2021.","DOI":"10.1007\/978-981-15-6321-8_11"},{"key":"1557_CR13","doi-asserted-by":"publisher","unstructured":"Thillaikkarasi, R. and Saravanan, S.: An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM. Journal of Medical Systems,. https:\/\/doi.org\/10.1007\/s10916-019-1223-7, 2019.","DOI":"10.1007\/s10916-019-1223-7"},{"key":"1557_CR14","doi-asserted-by":"publisher","unstructured":"Zhou, Z., He, Z. and Jia, Y.: AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing,. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.097, 2020.","DOI":"10.1016\/j.neucom.2020.03.097"},{"key":"1557_CR15","doi-asserted-by":"publisher","unstructured":"Khan, H., Shah, P.M., Shah, M.A., Islam, S. ul and Rodrigues, J.J.P.C.: Cascading handcrafted features and Convolutional Neural Network for IoT-enabled brain tumor segmentation. Computer Communications,. https:\/\/doi.org\/10.1016\/j.comcom.2020.01.013, 2020.","DOI":"10.1016\/j.comcom.2020.01.013"},{"key":"1557_CR16","doi-asserted-by":"publisher","unstructured":"Qamar, S., Jin, H., Zheng, R., Ahmad, P. and Usama, M.: A variant form of 3D-UNet for infant brain segmentation. Future Generation Computer Systems, 108. https:\/\/doi.org\/10.1016\/j.future.2019.11.021, 2020.","DOI":"10.1016\/j.future.2019.11.021"},{"key":"1557_CR17","doi-asserted-by":"publisher","unstructured":"Chen, W., Liu, B., Peng, S., Sun, J. and Qiao, X.: S3D-UNET: Separable 3D U-Net for brain tumor segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),. https:\/\/doi.org\/10.1007\/978-3-030-11726-9_32, 2019.","DOI":"10.1007\/978-3-030-11726-9_32"},{"key":"1557_CR18","doi-asserted-by":"publisher","unstructured":"Jia, Q. and Shu, H.: BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),. https:\/\/doi.org\/10.1007\/978-3-031-09002-8_1, 2022.","DOI":"10.1007\/978-3-031-09002-8_1"},{"key":"1557_CR19","doi-asserted-by":"publisher","unstructured":"Agravat, R.R. and Raval, M.S.: Brain tumor segmentation and survival prediction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),. https:\/\/doi.org\/10.1007\/978-3-030-46640-4_32, 2020.","DOI":"10.1007\/978-3-030-46640-4_32"},{"key":"1557_CR20","doi-asserted-by":"publisher","unstructured":"Balafar, M.A., Ramli, A.R., Saripan, M.I. and Mashohor, S.: Review of brain MRI image segmentation methods [Internet]. Artif Intell Rev. : Springer. p. 261\u201374. https:\/\/doi.org\/10.1007\/s10462-010-9155-0, 2010.","DOI":"10.1007\/s10462-010-9155-0"},{"key":"1557_CR21","unstructured":"Supraja, T., Sulthana, T.R., Pilani, S. and Veeramakali, D.C.: Brain Tumor Segmentation and Prediction using Fuzzy Neighborhood Learning Approach for 3D MRI Images. 0\u201316. , 2021."},{"key":"1557_CR22","doi-asserted-by":"publisher","unstructured":"Ozgur Cicek, Ahmed Abdulkabdir, Soeren S. Lienkamp, Thomas Brox, O.R.: 3D U_net. Medical Image Computing and Computer-Assisted Intervention, 424\u201332. https:\/\/doi.org\/10.1007\/978-3-319-46723-8, 2016.","DOI":"10.1007\/978-3-319-46723-8"},{"key":"1557_CR23","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P. and Brox, T.: U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28, 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1557_CR24","doi-asserted-by":"publisher","unstructured":"Li, X., Jiang, Y., Li, M., Zhang, J., Yin, S. and Luo, H.: MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation. Medical Physics, 50. https:\/\/doi.org\/10.1002\/mp.15933, 2023.","DOI":"10.1002\/mp.15933"},{"key":"1557_CR25","doi-asserted-by":"publisher","unstructured":"Wang, J., Gao, J., Ren, J., Luan, Z., Yu, Z., Zhao, Y. et al.: DFP-ResUNet:Convolutional Neural Network with a Dilated Convolutional Feature Pyramid for Multimodal Brain Tumor Segmentation. Computer Methods and Programs in Biomedicine,. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106208, 2021.","DOI":"10.1016\/j.cmpb.2021.106208"},{"key":"1557_CR26","doi-asserted-by":"publisher","unstructured":"Kalaiselvi, T. and Padmapriya, S.T.: Multimodal MRI Brain Tumor Segmentation-A ResNet-based U-Net approach. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques,. https:\/\/doi.org\/10.1016\/B978-0-323-91171-9.00013-2, 2021.","DOI":"10.1016\/B978-0-323-91171-9.00013-2"},{"key":"1557_CR27","doi-asserted-by":"publisher","unstructured":"Shehab, L.H., Fahmy, O.M., Gasser, S.M. and El-Mahallawy, M.S.: An efficient brain tumor image segmentation based on deep residual networks (ResNets). Journal of King Saud University - Engineering Sciences. https:\/\/doi.org\/10.1016\/j.jksues.2020.06.001, 2021.","DOI":"10.1016\/j.jksues.2020.06.001"},{"key":"1557_CR28","doi-asserted-by":"publisher","unstructured":"Yaqub, M., Jinchao, F., Zia, M.S., Arshid, K., Jia, K., Rehman, Z.U. et al.: State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images. Brain Sciences, 10, 1\u201319. https:\/\/doi.org\/10.3390\/brainsci10070427, 2020.","DOI":"10.3390\/brainsci10070427"},{"key":"1557_CR29","doi-asserted-by":"publisher","unstructured":"Brigato, L. and Iocchi, L.: A close look at deep learning with small data. Proceedings - International Conference on Pattern Recognition, 2490\u20137. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412492, 2020.","DOI":"10.1109\/ICPR48806.2021.9412492"},{"key":"1557_CR30","doi-asserted-by":"publisher","unstructured":"Goceri, E.: Medical image data augmentation: techniques, comparisons and interpretations [Internet]. Artif Intell Rev. : Springer Netherlands. https:\/\/doi.org\/10.1007\/s10462-023-10453-z, 2023.","DOI":"10.1007\/s10462-023-10453-z"},{"key":"1557_CR31","doi-asserted-by":"publisher","unstructured":"Rashid, T., Liu, H., Ware, J.B., Li, K., Romero, J.R., Fadaee, E. et al.: Deep learning based detection of enlarged perivascular spaces on brain MRI. Neuroimage: Reports, 3, 1\u201329. https:\/\/doi.org\/10.1016\/j.ynirp.2023.100162, 2023.","DOI":"10.1016\/j.ynirp.2023.100162"},{"key":"1557_CR32","doi-asserted-by":"publisher","unstructured":"Beser-Robles, M., Castell\u00e1-Malonda, J., Mart\u00ednez-Giron\u00e9s, P.M., Galiana-Bordera, A., Ferrer-Lozano, J., Ribas-Despuig, G. et al.: Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images. International Journal of Computer Assisted Radiology and Surgery, 19, 1743\u201351. https:\/\/doi.org\/10.1007\/s11548-024-03205-z, 2024.","DOI":"10.1007\/s11548-024-03205-z"},{"key":"1557_CR33","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Liu, Q. and Wang, Y.: Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15, 749\u201353. https:\/\/doi.org\/10.1109\/LGRS.2018.2802944, 2018.","DOI":"10.1109\/LGRS.2018.2802944"},{"key":"1557_CR34","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S. and Sun, J.: Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,. https:\/\/doi.org\/10.1109\/CVPR.2016.90, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1557_CR35","doi-asserted-by":"publisher","unstructured":"Liu, X., Jiang, Z., Roth, H.R., Anwar, S.M., Bonner, E.R., Mahtabfar, A. et al.: Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study. Neuro-Oncology Advances, 6, 1\u201311. https:\/\/doi.org\/10.1093\/noajnl\/vdae108, 2024.","DOI":"10.1093\/noajnl\/vdae108"},{"key":"1557_CR36","doi-asserted-by":"publisher","unstructured":"Liu, X., Bonner, E.R., Jiang, Z., Roth, H.R., Muhammad Anwar, S., Packer, R.J. et al.: Automatic Segmentation of Rare Pediatric Brain Tumors Using Knowledge Transfer From Adult Data. Proceedings - International Symposium on Biomedical Imaging, 2023-April. https:\/\/doi.org\/10.1109\/ISBI53787.2023.10230757, 2023.","DOI":"10.1109\/ISBI53787.2023.10230757"},{"key":"1557_CR37","doi-asserted-by":"publisher","unstructured":"Khened, M., Kori, A., Rajkumar, H., Krishnamurthi, G. and Srinivasan, B.: A generalized deep learning framework for whole-slide image segmentation and analysis. Scientific Reports, : Nature Publishing Group UK. 11, 1\u201314. https:\/\/doi.org\/10.1038\/s41598-021-90444-8, 2021.","DOI":"10.1038\/s41598-021-90444-8"},{"key":"1557_CR38","doi-asserted-by":"publisher","unstructured":"Berman, A.G., Orchard, W.R., Gehrung, M. and Markowetz, F.: SliDL: A toolbox for processing whole-slide images in deep learning. PLoS ONE, 18, 1\u201325. https:\/\/doi.org\/10.1371\/journal.pone.0289499, 2023.","DOI":"10.1371\/journal.pone.0289499"},{"key":"1557_CR39","doi-asserted-by":"publisher","unstructured":"Zhang, X., Han, L., Han, L., Chen, H., Dancey, D. and Zhang, D.: sMRI-PatchNet: A Novel Efficient Explainable Patch-Based Deep Learning Network for Alzheimer\u2019s Disease Diagnosis With Structural MRI. IEEE Access, 11, 108603\u201316. https:\/\/doi.org\/10.1109\/ACCESS.2023.3321220, 2023.","DOI":"10.1109\/ACCESS.2023.3321220"},{"key":"1557_CR40","doi-asserted-by":"publisher","unstructured":"Yalcinkaya, D.M., Youssef, K., Heydari, B., Zamudio, L., Dharmakumar, R. and Sharif, B.: Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 4072\u20138. https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629581, 2021.","DOI":"10.1109\/EMBC46164.2021.9629581"},{"key":"1557_CR41","doi-asserted-by":"publisher","unstructured":"Mart\u00ed-Bonmat\u00ed, L., Alberich-Bayarri, \u00c1., Ladenstein, R., Blanquer, I., Segrelles, J.D., Cerd\u00e1-Alberich, L. et al.: PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. European Radiology Experimental,. https:\/\/doi.org\/10.1186\/s41747-020-00150-9, 2020.","DOI":"10.1186\/s41747-020-00150-9"},{"key":"1557_CR42","doi-asserted-by":"publisher","unstructured":"Barkovich, A.J., Krischer, J., Kun, L.F., Packer, R., Zimmerman, R.A., Freeman, C.R. et al.: Brain stem gliomas: A classification system based on magnetic resonance imaging. Pediatric Neurosurgery,. https:\/\/doi.org\/10.1159\/000120511, 1990.","DOI":"10.1159\/000120511"},{"key":"1557_CR43","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez Pat\u00f3n, M., Cerd\u00e1 Alberich, L., Sang\u00fcesa Nebot, C., Mart\u00ednez de las Heras, B., Veiga Canuto, D., Ca\u00f1ete Nieto, A. et al.: MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging. Journal of Digital Imaging, : Springer International Publishing. 34, 1134\u201345. https:\/\/doi.org\/10.1007\/s10278-021-00512-8, 2021.","DOI":"10.1007\/s10278-021-00512-8"},{"key":"1557_CR44","doi-asserted-by":"publisher","unstructured":"Beare, R., Lowekamp, B. and Yaniv, Z.: Image segmentation, registration and characterization in R with simpleITK. Journal of Statistical Software,. https:\/\/doi.org\/10.18637\/jss.v086.i08, 2018.","DOI":"10.18637\/jss.v086.i08"},{"key":"1557_CR45","doi-asserted-by":"publisher","unstructured":"Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A. et al.: N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging,. https:\/\/doi.org\/10.1109\/TMI.2010.2046908, 2010.","DOI":"10.1109\/TMI.2010.2046908"},{"key":"1557_CR46","doi-asserted-by":"publisher","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M.A. and Pluim, J.P.W.: elastix: A Toolbox for Intensity-Based Medical Image Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING, 29. https:\/\/doi.org\/10.1109\/TMI.2009.2035616, 2010.","DOI":"10.1109\/TMI.2009.2035616"},{"key":"1557_CR47","unstructured":"Kingma, D.P. and Ba, J.L.: Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1\u201315. , 2015."},{"key":"1557_CR48","doi-asserted-by":"publisher","unstructured":"Milletari, F., Navab, N. and Ahmadi, S.A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016,. https:\/\/doi.org\/10.1109\/3DV.2016.79, 2016.","DOI":"10.1109\/3DV.2016.79"},{"key":"1557_CR49","doi-asserted-by":"publisher","unstructured":"Salehi, S.S.M., Erdogmus, D. and Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10541 LNCS, 379\u201387. https:\/\/doi.org\/10.1007\/978-3-319-67389-9_44, 2017.","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"1557_CR50","unstructured":"Nair, V. and Hinton, G.E.: Rectified linear units improve Restricted Boltzmann machines. ICML 2010 - Proceedings, 27th International Conference on Machine Learning,. , 2010."},{"key":"1557_CR51","doi-asserted-by":"crossref","unstructured":"Misra, D.: Mish: A Self Regularized Non-Monotonic Activation Function. , 2019.","DOI":"10.5244\/C.34.191"},{"key":"1557_CR52","doi-asserted-by":"publisher","unstructured":"Veiga-Canuto, D., Cerd\u00e0-Alberich, L., Jim\u00e9nez-Pastor, A., Carot Sierra, J.M., Gomis-Maya, A., Sang\u00fcesa-Nebot, C. et al.: Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images. Cancers, 15, 1\u201313. 2023, .https:\/\/doi.org\/10.3390\/cancers15051622","DOI":"10.3390\/cancers15051622"},{"key":"1557_CR53","doi-asserted-by":"publisher","unstructured":"Veiga-Canuto, D., Cerd\u00e0-Alberich, L., Sang\u00fcesa Nebot, C., Mart\u00ednez de las Heras, B., P\u00f6tschger, U., Gabelloni, M. et al.: Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images. Cancers, 14. https:\/\/doi.org\/10.3390\/cancers14153648, 2022.","DOI":"10.3390\/cancers14153648"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01557-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01557-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01557-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:46:42Z","timestamp":1771526802000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01557-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["1557"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01557-9","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"assertion":[{"value":"7 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study has been approved by the Hospital\u2019s Ethics Committee (The Ethics Committee for Investigation with medicinal products of the University and Polytechnic La Fe Hospital, ethic code: 2018\/0228).The research conducted respects fundamental ethical principles, including those reflected in the Charter of Fundamental Rights of the European Union (7 th December 2000) and worldwide: Dignity, Freedom, Equality, Solidarity, Citizens\u2019 Rights and Justice. Additionally, the project is in accordance with the European Human Rights Convention, especially with regard to Privacy and Autonomy.PRIMAGE project did not work directly with any human being, as only patients\u2019 data was handled. The research did not involve physical intervention in the study\u2019s participants. The research did not involve collection of biological samples and as patients were already treated.PRIMAGE project has the favorable dictum of the Ethics Committee Research from the University and Polytechnic Hospital La Fe, the St. Anna Kinderkrebsforschung Children\u2019s Cancer Research Institute (CCRI), the University Clinic of Koeln, and the approval of the AEMPS (Spanish Agency of Medicine and Health products) and the University Hospital of Pisa (UNIPI).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}