{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T02:52:49Z","timestamp":1781491969636,"version":"3.54.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00521-025-11662-z","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T07:51:40Z","timestamp":1759477900000},"page":"27137-27149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Brain tumor segmentation capabilities of 3D deep learning architectures (U-Net, V-Net, Attention U-Net, ResNet-based U-Net, Transformer-based model)"],"prefix":"10.1007","volume":"37","author":[{"given":"Otabek","family":"Puladjonov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Pooja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ambuj","family":"Aggarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"11662_CR1","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2014MICCAI 2015. Springer, Cham, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"11662_CR2","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV). IEEE, Stanford, USA, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"11662_CR3","unstructured":"Oktay O et al (2018) Attention U-Net: learning where to look for pancreas segmentation. arXiv preprint arXiv:1804.03999"},{"key":"11662_CR4","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), IEEE, Las Vegas, USA, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11662_CR5","unstructured":"Isensee F et al (2017) Automatic brain tumor segmentation using ensembles of deep convolutional neural networks in the BRATS 2017 winning solution. arXiv preprint arXiv:1802.10508"},{"key":"11662_CR6","unstructured":"Myronenko A (2019) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention\u2014MICCAI 2019, Springer, Cham, pp 379\u2013388"},{"key":"11662_CR7","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1146\/annurev.bioeng.2.1.315","volume":"2","author":"DL Pham","year":"2000","unstructured":"Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315\u2013337","journal-title":"Annu Rev Biomed Eng"},{"issue":"1","key":"11662_CR8","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on data augmentation techniques for deep learning. J Big Data 6(1):60","journal-title":"J Big Data"},{"key":"11662_CR9","unstructured":"Greenspan H, van Ginneken J, Kwitt R, Nielsen M (2016) Non-linear transformations for medical image augmentation. In: International workshop on machine learning in medical imaging, Springer, Cham, pp 16\u201330"},{"key":"11662_CR10","unstructured":"Hatamizadeh J, Tang M, Glocker BJ, Roth H (2020) Direct uncertainty-aware variational autoencoder for medical image segmentation. In: Medical image computing and computer-assisted intervention\u2014MICCAI 2020, Springer, Cham, pp 390\u2013399"},{"key":"11662_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh H, Tang Y, Nath D (2022) Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV), IEEE, Waikoloa, USA, pp 1757\u20131767","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"11662_CR12","volume-title":"Computer vision: ECCV 2022 workshops, ECCV 2022 lecture notes in computer science","author":"H Cao","year":"2023","unstructured":"Cao H et al (2023) Swin-Unet: unet-like pure transformer for medical image segmentation. In: Karlinsky L, Michaeli T, Nishino K (eds) Computer vision: ECCV 2022 workshops, ECCV 2022 lecture notes in computer science, vol 13803. Springer, Cham"},{"key":"11662_CR13","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE international conference on computer vision, IEEE, Santiago, Chile, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"11662_CR14","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV), IEEE, Stanford, USA, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"11662_CR15","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning, 2nd edn. Springer, New York","edition":"2"},{"key":"11662_CR16","doi-asserted-by":"crossref","unstructured":"Salehi S, Erdogmus D, Gholipour A (2017) Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: International workshop on machine learning in medical imaging, Springer, Cham, pp 379\u2013387","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"11662_CR17","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"11662_CR18","unstructured":"Shahriari B, Swersky K, Wang Z, Adams RP, de Freitas N (2013) Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, pp 703\u2013711"},{"issue":"1","key":"11662_CR19","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"11662_CR20","doi-asserted-by":"crossref","unstructured":"Huttenlocher D, Rucklidge G, Felzenszwalb P (1993) Comparing images using the Hausdorff distance. In: Proceedings of the 1993 IEEE Computer society conference on computer vision and pattern recognition, IEEE, New York, USA, pp 223\u2013229","DOI":"10.1109\/34.232073"},{"key":"11662_CR21","doi-asserted-by":"crossref","unstructured":"Futrega M, Milesi A, Marcinkiewicz M, Ribalta P (2022) Optimized U-Net for brain tumor segmentation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI), Springer, Cham, pp 457\u2013468","DOI":"10.1007\/978-3-031-09002-8_2"},{"key":"11662_CR22","first-page":"259","volume-title":"Deep learning and visual artificial intelligence, ICDLAI, algorithms for intelligent systems","author":"D Uppal","year":"2024","unstructured":"Uppal D, Ananda MK, Prakash MB, Prakash S (2024) Volumetric brain tumor segmentation using V-Net. In: Goar V, Sharma A, Shin J, Mridha MF (eds) Deep learning and visual artificial intelligence, ICDLAI, algorithms for intelligent systems. Springer, Singapore, pp 259\u2013273"},{"key":"11662_CR23","doi-asserted-by":"crossref","unstructured":"Neyaz Z, Mittal H (2024) Integrating 3D U-Net and attention U-Net for brain tumor segmentation: performance evaluation on BRATS 2021 dataset. In: 2024 15th International conference on computing communication and networking technologies (ICCCNT), IEEE, Kamand, India, pp 1\u20136","DOI":"10.1109\/ICCCNT61001.2024.10724970"},{"key":"11662_CR24","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/B978-0-323-91171-9.00013-2","volume-title":"Brain tumor MRI image segmentation using deep learning techniques","author":"T Kalaiselvi","year":"2022","unstructured":"Kalaiselvi T, Padmapriya ST (2022) Multimodal MRI brain tumor segmentation: a ResNet-based U-Net approach. In: Chaki J (ed) Brain tumor MRI image segmentation using deep learning techniques. Academic Press, Cambridge, pp 123\u2013135"},{"key":"11662_CR25","doi-asserted-by":"publisher","first-page":"26922","DOI":"10.1109\/ACCESS.2017.2776349","volume":"5","author":"M Versaci","year":"2017","unstructured":"Versaci M, Morabito FC, Angiulli G (2017) Adaptive image contrast enhancement by computing distances into a 4-dimensional fuzzy unit hypercube. IEEE Access 5:26922\u201326931. https:\/\/doi.org\/10.1109\/ACCESS.2017.2776349","journal-title":"IEEE Access"},{"issue":"10","key":"11662_CR26","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024. https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans Med Imaging"},{"key":"11662_CR27","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS et al (2017) Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat Sci Data 4:170117. https:\/\/doi.org\/10.1038\/sdata.2017.117","journal-title":"Nat Sci Data"}],"updated-by":[{"DOI":"10.1007\/s00521-026-12176-y","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11662-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11662-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11662-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T02:22:14Z","timestamp":1781490134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11662-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,3]]},"references-count":27,"journal-issue":{"issue":"32","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["11662"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11662-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,3]]},"assertion":[{"value":"9 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised to correct the first author name","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":8,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":9,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":10,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-026-12176-y","URL":"https:\/\/doi.org\/10.1007\/s00521-026-12176-y","order":11,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"\u201cNot applicable\u201d The dataset used for this work is publically available at\n                      \n                      . The required citations for the dataset are included in the reference.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}]}}