{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T15:28:15Z","timestamp":1785598095432,"version":"3.56.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","award":["TG\/18\/5\/34111"],"award-info":[{"award-number":["TG\/18\/5\/34111"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","award":["PG\/16\/78\/32402"],"award-info":[{"award-number":["PG\/16\/78\/32402"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ERC IMI","award":["101005122"],"award-info":[{"award-number":["101005122"]}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["952172"],"award-info":[{"award-number":["952172"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC\/PC\/21013"],"award-info":[{"award-number":["MC\/PC\/21013"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000288","name":"Royal Society","doi-asserted-by":"crossref","award":["IEC\/NSFC\/211235"],"award-info":[{"award-number":["IEC\/NSFC\/211235"]}],"id":[{"id":"10.13039\/501100000288","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Imperial College Undergraduate Research Opportunities Programme"},{"name":"NVIDIA Academic Hardware Grant Program"},{"DOI":"10.13039\/100001003","name":"Boehringer Ingelheim","doi-asserted-by":"publisher","award":["SABER"],"award-info":[{"award-number":["SABER"]}],"id":[{"id":"10.13039\/100001003","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013342","name":"NIHR Imperial Biomedical Research Centre","doi-asserted-by":"publisher","award":["RDA01"],"award-info":[{"award-number":["RDA01"]}],"id":[{"id":"10.13039\/501100013342","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/V023799\/1"],"award-info":[{"award-number":["MR\/V023799\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003086","name":"Basque Government","doi-asserted-by":"crossref","award":["IT1456-22"],"award-info":[{"award-number":["IT1456-22"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001872","name":"Centro para el Desarrollo Tecnol\u00f3gico Industrial","doi-asserted-by":"publisher","award":["AI4ES"],"award-info":[{"award-number":["AI4ES"]}],"id":[{"id":"10.13039\/501100001872","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.<\/jats:p>","DOI":"10.1007\/s00521-022-08016-4","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T15:05:03Z","timestamp":1668697503000},"page":"22071-22085","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation"],"prefix":"10.1007","volume":"35","author":[{"given":"Hao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Nan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Del Ser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7344-7733","authenticated-orcid":false,"given":"Guang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"8016_CR1","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3389\/fnins.2020.00282","volume":"14","author":"A Jungo","year":"2020","unstructured":"Jungo A, Balsiger F, Reyes M (2020) Analyzing the quality and challenges of uncertainty estimations for brain tumor segmentation. Front Neurosci 14:282. https:\/\/doi.org\/10.3389\/fnins.2020.00282","journal-title":"Front Neurosci"},{"issue":"2","key":"8016_CR2","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1109\/TNNLS.2020.2995800","volume":"32","author":"K Muhammad","year":"2021","unstructured":"Muhammad K, Khan S, Ser JD, de Albuquerque VHC (2021) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn Syst 32(2):507\u2013522. https:\/\/doi.org\/10.1109\/TNNLS.2020.2995800","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8016_CR3","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). Boston, MA, USA: IEEE. pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"8016_CR4","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2015, vol 9351. Springer International Publishing, Cham, pp 234\u2013241"},{"key":"8016_CR5","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1007\/978-3-319-60964-5_44","volume-title":"Medical image understanding and analysis","author":"H Dong","year":"2017","unstructured":"Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Vald\u00e9s Hern\u00e1ndez M, Gonz\u00e1lez-Castro V (eds) Medical image understanding and analysis, vol 723. Springer International Publishing, Cham, pp 506\u2013517"},{"key":"8016_CR6","doi-asserted-by":"crossref","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, et\u00a0al. (2021) TransUNet: Transformers make strong encoders for medical image segmentation. arXiv:2102.04306 [cs]","DOI":"10.1109\/IGARSS46834.2022.9883628"},{"key":"8016_CR7","unstructured":"Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, et\u00a0al. (2019) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv:1811.02629 [cs, stat]"},{"issue":"6","key":"8016_CR8","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.6.064006","volume":"7","author":"S M\u00fcller","year":"2020","unstructured":"M\u00fcller S, Weickert J, Graf N (2020) Robustness of brain tumor segmentation. J Med Imaging 7(6):064006","journal-title":"J Med Imaging"},{"issue":"6","key":"8016_CR9","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1038\/s41592-019-0406-y","volume":"16","author":"K Das","year":"2019","unstructured":"Das K, Krzywinski M, Altman N (2019) Quantile regression. Nat Methods 16(6):451\u2013452. https:\/\/doi.org\/10.1038\/s41592-019-0406-y","journal-title":"Nat Methods"},{"key":"8016_CR10","doi-asserted-by":"crossref","unstructured":"Hinton GE, van Camp D (1993) Keeping the neural networks simple by minimizing the description length of the weights. In: proceedings of the sixth annual conference on computational learning theory - COLT \u201993. Santa Cruz, California, USA: ACM Press. pp 5\u201313","DOI":"10.1145\/168304.168306"},{"issue":"3","key":"8016_CR11","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay DJC (1992) A practical bayesian framework for backpropagation networks. Neural Comput 4(3):448\u2013472. https:\/\/doi.org\/10.1162\/neco.1992.4.3.448","journal-title":"Neural Comput"},{"key":"8016_CR12","unstructured":"Hernandez-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: Bach F, Blei D (eds). proceedings of the 32nd international conference on machine learning. vol.\u00a037 of proceedings of machine learning research. Lille, France: PMLR. pp 1861\u20131869"},{"key":"8016_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101557","volume":"59","author":"T Nair","year":"2020","unstructured":"Nair T, Precup D, Arnold DL, Arbel T (2020) Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med Image Anal 59:101557. https:\/\/doi.org\/10.1016\/j.media.2019.101557","journal-title":"Med Image Anal"},{"key":"8016_CR14","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, PMLR. pp 1050-1059"},{"key":"8016_CR15","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Adv Neural Inform Proess Syst, 30."},{"key":"8016_CR16","doi-asserted-by":"crossref","unstructured":"Kendall A, Badrinarayanan V, Cipolla R (2017) Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: Procedings of the British machine vision conference 2017. London, UK: British Machine Vision Association. p\u00a057","DOI":"10.5244\/C.31.57"},{"key":"8016_CR17","unstructured":"Sensoy M, Kaplan L, Kandemir M (2018) Evidential Deep Learning to Quantify Classification Uncertainty. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, editors. Advances in Neural Information Processing Systems. vol.\u00a031. Curran Associates, Inc"},{"key":"8016_CR18","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neunet.2020.12.011","volume":"135","author":"T Tsiligkaridis","year":"2021","unstructured":"Tsiligkaridis T (2021) Information aware max-norm dirichlet networks for predictive uncertainty estimation. Neural Netw 135:105\u2013114. https:\/\/doi.org\/10.1016\/j.neunet.2020.12.011","journal-title":"Neural Netw"},{"issue":"9","key":"8016_CR19","doi-asserted-by":"publisher","first-page":"6376","DOI":"10.1007\/s10489-021-02327-0","volume":"51","author":"Z Tong","year":"2021","unstructured":"Tong Z, Xu P, Den\u0153ux T (2021) Evidential fully convolutional network for semantic segmentation. Appl Intell 51(9):6376\u20136399. https:\/\/doi.org\/10.1007\/s10489-021-02327-0arXiv:2103.13544. [cs]","journal-title":"Appl Intell"},{"key":"8016_CR20","unstructured":"Guo C, Pleiss G, Sun Y, Weinberger KQ. (2017) On calibration of modern neural networks. In: International conference on machine learning"},{"issue":"12","key":"8016_CR21","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash A, Wells WM, Tempany CM, Abolmaesumi P, Kapur T (2020) Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans Med Imaging 39(12):3868\u20133878. https:\/\/doi.org\/10.1109\/TMI.2020.3006437","journal-title":"IEEE Trans Med Imaging"},{"key":"8016_CR22","doi-asserted-by":"crossref","unstructured":"Gawlikowski J, Tassi CRN, Ali M, Lee J, Humt M, Feng J, et\u00a0al. (2022) A survey of uncertainty in deep neural networks. arXiv","DOI":"10.1007\/s10462-023-10562-9"},{"key":"8016_CR23","unstructured":"Kohl SAA, Romera-Paredes B, Meyer C, De\u00a0Fauw J, Ledsam JR, Maier-Hein KH, et\u00a0al. (2019) A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31."},{"key":"8016_CR24","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-540-44792-4_4","volume-title":"Classic works of the dempster-shafer theory of belief functions","author":"AP Dempster","year":"2008","unstructured":"Dempster AP (2008) A generalization of Bayesian inference. In: Yager RR, Liu L (eds) Classic works of the dempster-shafer theory of belief functions. Springer, Berlin, Heidelberg, pp 73\u2013104"},{"key":"8016_CR25","doi-asserted-by":"crossref","unstructured":"Zou K, Yuan X, Shen X, Wang M, Fu H (2022) TBraTS: Trusted brain tumor segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham.","DOI":"10.1007\/978-3-031-16452-1_48"},{"key":"8016_CR26","doi-asserted-by":"crossref","unstructured":"J\u00f8sang A (2016) Subjective logic. Artificial intelligence: foundations, theory, and algorithms. Springer International Publishing, Cham","DOI":"10.1007\/978-3-319-42337-1"},{"key":"8016_CR27","volume-title":"Continuous multivariate distributions","author":"S Kotz","year":"2005","unstructured":"Kotz S, Balakrishnan N, Johnson NL (2005) Continuous multivariate distributions, vol 1. Wiley, Hoboken"},{"key":"8016_CR28","first-page":"503","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"M Morales","year":"2008","unstructured":"Morales M (2008) Construction of the digamma function by derivative definition. International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 503\u2013513"},{"key":"8016_CR29","unstructured":"Malinin A, Gales M (2019) Reverse KL-divergence training of prior networks: improved uncertainty and adversarial robustness. Advances in Neural Information Processing Systems"},{"issue":"1","key":"8016_CR30","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. Sci Data 4(1):170117. https:\/\/doi.org\/10.1038\/sdata.2017.117","journal-title":"Sci Data"},{"issue":"10","key":"8016_CR31","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":"8016_CR32","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-031-12053-4_24","volume-title":"Medical image understanding and analysis","author":"H Li","year":"2022","unstructured":"Li H, Nan Y, Yang G (2022) LKAU-Net: 3D large-kernel attention-based u-net for automatic MRI brain tumor segmentation. In: Yang G, Aviles-Rivero A, Roberts M, Sch\u00f6nlieb CB (eds) Medical image understanding and analysis, vol 13413. Springer International Publishing, Cham, pp 313\u2013327"},{"key":"8016_CR33","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/978-3-030-72087-2_11","volume-title":"Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries","author":"F Isensee","year":"2021","unstructured":"Isensee F, J\u00e4ger PF, Full PM, Vollmuth P, Maier-Hein KH (2021) nnU-net for brain tumor segmentation. In: Crimi A, Bakas S (eds) Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries, vol 12659. Springer International Publishing, Cham, pp 118\u2013132"},{"key":"8016_CR34","doi-asserted-by":"crossref","unstructured":"Li H, Nan Y, Del\u00a0Ser J, Yang G (2022) Large-kernel attention for 3D medical image segmentation. arXiv","DOI":"10.1007\/s12559-023-10126-7"},{"key":"8016_CR35","unstructured":"Ovadia Y, Fertig E, Ren J, Nado Z, Sculley D, Nowozin S, et\u00a0al. (2019) Can you trust your models uncertainty? evaluating predictive uncertainty under dataset shift. In: Wallach H, Larochelle H, Beygelzimer A, dAlch\u00e9-Buc F, Fox E, Garnett R, (eds). Advances in Neural Information Processing Systems. vol.\u00a032. Curran Associates, Inc"},{"key":"8016_CR36","doi-asserted-by":"crossref","unstructured":"Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, et\u00a0al. (2021) QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation \u2013 Analysis of Ranking Metrics and Benchmarking Results. arXiv","DOI":"10.59275\/j.melba.2022-354b"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08016-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08016-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08016-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:02:02Z","timestamp":1694876522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08016-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":36,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8016"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08016-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"10 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}