{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:41:22Z","timestamp":1772613682828,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06351-6","type":"journal-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T06:02:58Z","timestamp":1627711378000},"page":"211-225","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Glioma segmentation of optimized 3D U-net and prediction of multi-modal survival time"],"prefix":"10.1007","volume":"34","author":[{"given":"Qihong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8643-7251","authenticated-orcid":false,"given":"Kai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Boluf\u00e9-R\u00f6hler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"6351_CR1","doi-asserted-by":"crossref","unstructured":"Aziz M. J., Tehrani zade A. A., Farnia P, Alimohamadi M, Makkiabadi B, Ahmadian A, Alirezaie J (2021). Accurate automatic glioma segmentation in brain mri images based on capsnet. bioRxiv","DOI":"10.1101\/2021.07.03.450931"},{"key":"6351_CR2","doi-asserted-by":"publisher","first-page":"170117","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, Freymann J, Farahani K, Davatzikos C (2017a) Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection. The cancer imaging archive. Nat Sci Data 4:170117","journal-title":"Nat Sci Data"},{"key":"6351_CR3","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J.\u00a0S., Freymann J, Farahani K, Davatzikos C (2017b). Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection. The cancer imaging archive"},{"key":"6351_CR4","doi-asserted-by":"publisher","first-page":"170117","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, Freymann JB, Farahani K, Davatzikos C (2017c) Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Nat Sci Data 4:170117","journal-title":"Nat Sci Data"},{"key":"6351_CR5","unstructured":"Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara R.\u00a0T., Berger C, Ha S.\u00a0M., Rozycki M, et\u00a0al (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629"},{"key":"6351_CR6","first-page":"351","volume-title":"Optimization for machine learning","author":"L Bottou","year":"2012","unstructured":"Bottou L, Bousquet O (2012) The tradeoffs of large scale learning. In: Suvrit S, Nowozin S, Wright SJ (eds) Optimization for machine learning. MIT Press, London, pp 351\u2013368"},{"issue":"1","key":"6351_CR7","doi-asserted-by":"publisher","first-page":"E2","DOI":"10.3171\/2014.10.FOCUS14732","volume":"38","author":"BS Carter","year":"2015","unstructured":"Carter BS, Chiocca AE, Lonser R, Kaye AH, de Tribolet N (2015) Introduction: a focus on low-grade glioma. Neurosurg Focus 38(1):E2","journal-title":"Neurosurg Focus"},{"key":"6351_CR8","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek,\u00d6, Abdulkadir A, Lienkamp S.\u00a0S, Brox T, Ronneberger O (2016). 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention, pp. 424\u2013432. Springer","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"6351_CR9","first-page":"2843","volume":"25","author":"D Ciresan","year":"2012","unstructured":"Ciresan D, Giusti A, Gambardella LM, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. Adv Neural Inf Process Syst 25:2843\u20132851","journal-title":"Adv Neural Inf Process Syst"},{"key":"6351_CR10","doi-asserted-by":"crossref","unstructured":"Dand\u0131l E (2017). Implementation and comparison of image segmentation methods for detection of brain tumors on mr images. In: IEEE 2017 International Conference on Computer Science and Engineering (UBMK), pp. 1025\u20131029","DOI":"10.1109\/UBMK.2017.8093425"},{"key":"6351_CR11","doi-asserted-by":"crossref","unstructured":"Deng W, Xiao X, Deng H, Liu J (2010) MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. In: 3rd International Conference on Biomedical Engineering and Informatics. pp 393\u2013396","DOI":"10.1109\/BMEI.2010.5639536"},{"key":"6351_CR12","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"L Dice","year":"1945","unstructured":"Dice L (1945) Measures of the amount of ecologic association between species. J Ecol 26:297\u2013302","journal-title":"J Ecol"},{"key":"6351_CR13","unstructured":"Diederik K, Ba J (2014). Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"6351_CR14","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12:2121\u20132159","journal-title":"JMLR"},{"key":"6351_CR15","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT press, Cambridge"},{"issue":"18","key":"6351_CR16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/0920-9964(96)85575-7","volume":"2\u20133","author":"R Guillemaud","year":"1996","unstructured":"Guillemaud R, Marais P, Zisserman A, McDonald B, Crow T, Brady M (1996) A three dimensional mid sagittal plane for brain asymmetry measurement. Schizophrenia Res 2\u20133(18):183\u2013184","journal-title":"Schizophrenia Res"},{"issue":"3","key":"6351_CR17","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.aej.2020.01.033","volume":"59","author":"Y Hamed","year":"2020","unstructured":"Hamed Y, Alzahrani AI, A\u2019fza S, Mustaffa Z, Ismail MC, Eng KK (2020) Two steps hybrid calibration algorithm of support vector regression and k-nearest neighbors. Alexandria Eng J 59(3):1181\u20131190","journal-title":"Alexandria Eng J"},{"key":"6351_CR18","unstructured":"Hinton G.\u00a0E., Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R.\u00a0R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"issue":"1","key":"6351_CR19","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TMI.2015.2461533","volume":"35","author":"T Huynh","year":"2015","unstructured":"Huynh T, Gao Y, Kang J, Wang L, Zhang P, Lian J, Shen D (2015) Estimating ct image from mri data using structured random forest and auto-context model. IEEE Trans Med Imaging 35(1):174\u2013183","journal-title":"IEEE Trans Med Imaging"},{"key":"6351_CR20","doi-asserted-by":"crossref","unstructured":"Kaldera H, Gunasekara S, Dissanayake M (2019). Mri based glioma segmentation using deep learning algorithms. In: 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 51\u201356","DOI":"10.23919\/SCSE.2019.8842668"},{"key":"6351_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 36:61\u201378","journal-title":"Med Image Anal"},{"key":"6351_CR22","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neuroimage.2016.01.024","volume":"129","author":"J Kleesiek","year":"2016","unstructured":"Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, Biller A (2016) Deep mri brain extraction: a 3d convolutional neural network for skull stripping. NeuroImage 129:460\u2013469","journal-title":"NeuroImage"},{"issue":"10","key":"6351_CR23","first-page":"255","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. Hand Brain Theory Neural Netw 3361(10):255\u2013258","journal-title":"Hand Brain Theory Neural Netw"},{"key":"6351_CR24","doi-asserted-by":"crossref","unstructured":"Li M, Zhang L, Xiang Z, Castillo E, Guerrero T (2016). An improved fuzzy c-means algorithm for brain mri image segmentation. In: IEEE 2016 International Conference on Progress in Informatics and Computing (PIC), pp. 336\u2013339","DOI":"10.1109\/PIC.2016.7949521"},{"issue":"1","key":"6351_CR25","doi-asserted-by":"publisher","first-page":"5467","DOI":"10.1038\/s41598-017-05848-2","volume":"7","author":"Z Li","year":"2017","unstructured":"Li Z, Wang Y, Yu J, Guo Y, Cao W (2017) Deep learning based radiomics (dlr) and its usage in noninvasive idh1 prediction for low grade glioma. Sci Rep 7(1):5467","journal-title":"Sci Rep"},{"issue":"3","key":"6351_CR26","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):18\u201322","journal-title":"R News"},{"key":"6351_CR27","doi-asserted-by":"crossref","unstructured":"Menze B, Isensee F, Wiest R, Wiestler B, Maier-Hein K, Reyes M, Bakas S (2020). Analyzing magnetic resonance imaging data from glioma patients using deep learning. In: Computerized Medical Imaging and Graphics, pp. 101828","DOI":"10.1016\/j.compmedimag.2020.101828"},{"issue":"10","key":"6351_CR28","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 JS et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"key":"6351_CR29","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cviu.2017.04.002","volume":"164","author":"F Milletari","year":"2017","unstructured":"Milletari F, Ahmadi S-A, Kroll C, Plate A, Rozanski V, Maiostre J, Levin J, Dietrich O, Ertl-Wagner B, B\u00f6tzel K et al (2017) Hough-cnn: deep learning for segmentation of deep brain regions in mri and ultrasound. Comput Vis Image Underst. 164:92\u2013102","journal-title":"Comput Vis Image Underst."},{"issue":"7","key":"6351_CR30","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1093\/neuonc\/nou087","volume":"16","author":"QT Ostrom","year":"2014","unstructured":"Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner MC, Walsh KM, Wrensch MR, Barnholtz-Sloan JS (2014) The epidemiology of glioma in adults: a \u201cstate of the science\u201d review. Neuro-Oncol. 16(7):896\u2013913","journal-title":"Neuro-Oncol."},{"issue":"3","key":"6351_CR31","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/s11910-010-0105-7","volume":"10","author":"N Pouratian","year":"2010","unstructured":"Pouratian N, Schiff D (2010) Management of low-grade glioma. Curr Neurol Neurosci Rep 10(3):224\u2013231","journal-title":"Curr Neurol Neurosci Rep"},{"key":"6351_CR32","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015). U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6351_CR33","doi-asserted-by":"publisher","first-page":"84","DOI":"10.3389\/fncom.2019.00084","volume":"13","author":"JD Rudie","year":"2019","unstructured":"Rudie JD, Weiss DA, Saluja R, Rauschecker AM, Wang J, Sugrue L, Bakas S, Colby JB (2019) Multi-disease segmentation of gliomas and white matter hyperintensities in the brats data using a 3d convolutional neural network. Front Comput Neurosci 13:84","journal-title":"Front Comput Neurosci"},{"issue":"4","key":"6351_CR34","doi-asserted-by":"publisher","first-page":"145","DOI":"10.4103\/2229-3485.86872","volume":"2","author":"R Singh","year":"2011","unstructured":"Singh R, Mukhopadhyay K (2011) Survival analysis in clinical trials: basics and must know areas. Perspect Clin Res 2(4):145\u2013148","journal-title":"Perspect Clin Res"},{"issue":"2","key":"6351_CR35","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.neuroimage.2004.06.022","volume":"23","author":"A Stadlbauer","year":"2004","unstructured":"Stadlbauer A, Moser E, Gruber S, Buslei R, Nimsky C, Fahlbusch R, Ganslandt O (2004) Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1h-mrsi metabolites in gliomas. Neuroimage 23(2):454\u2013461","journal-title":"Neuroimage"},{"issue":"2","key":"6351_CR36","doi-asserted-by":"publisher","first-page":"e190011","DOI":"10.1148\/ryai.2020190011","volume":"2","author":"S Wu","year":"2020","unstructured":"Wu S, Li H, Quang D, Guan Y (2020) Three-plane-assembled deep learning segmentation of gliomas. Radiol Artif Intell 2(2):e190011","journal-title":"Radiol Artif Intell"},{"key":"6351_CR37","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s12880-019-0369-6","volume":"19","author":"Y Wu","year":"2019","unstructured":"Wu Y, Zhao Z, Wu W, Lin Y, Wang M (2019) Automatic glioma segmentation based on adaptive superpixel. BMC Med Imaging 19:73","journal-title":"BMC Med Imaging"},{"key":"6351_CR38","doi-asserted-by":"crossref","unstructured":"Zhang S, Zong M, Sun K, Liu Y, Cheng D (2014). Efficient knn algorithm based on graph sparse reconstruction. In: International Conference on Advanced Data Mining and Applications, pp. 356\u2013369. Springer","DOI":"10.1007\/978-3-319-14717-8_28"},{"issue":"5","key":"6351_CR39","first-page":"176","volume":"53","author":"W Zhang","year":"2017","unstructured":"Zhang W, Wang X, Li Z, Qu Y (2017) Brain tumor image segmentation based on c-v model optimized by watershed transformation. Comput Eng Appl 53(5):176\u2013180","journal-title":"Comput Eng Appl"},{"issue":"1","key":"6351_CR40","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/42.906424","volume":"20","author":"Y Zhang","year":"2001","unstructured":"Zhang Y, Brady M, Smith S (2001) Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45\u201357","journal-title":"IEEE Trans Med Imaging"},{"key":"6351_CR41","unstructured":"Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings MICCAI-BRATS pp. 36\u201339"},{"issue":"2","key":"6351_CR42","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","volume":"11","author":"KH Zou","year":"2004","unstructured":"Zou KH, Warfield SK, Bharatha AB, Tempany CM, Kaus MR, Haker SJ, Wells WMW, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11(2):178\u2013189","journal-title":"Acad Radiol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06351-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06351-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06351-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T11:13:17Z","timestamp":1641813197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06351-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,31]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6351"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06351-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,31]]},"assertion":[{"value":"18 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}