{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:09:15Z","timestamp":1781518155044,"version":"3.54.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14\u00a0months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using S\u00f8rensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation.<\/jats:p>","DOI":"10.1186\/s12859-022-04794-9","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T11:07:09Z","timestamp":1656068829000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture"],"prefix":"10.1186","volume":"23","author":[{"given":"Zeeshan","family":"Shaukat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qurat ul Ain","family":"Farooq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuangbai","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saqib","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"issue":"1","key":"4794_CR1","doi-asserted-by":"publisher","first-page":"10930","DOI":"10.1038\/s41598-021-90428-8","volume":"11","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh R, et al. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep. 2021;11(1):10930.","journal-title":"Sci Rep"},{"issue":"5 Suppl","key":"4794_CR2","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1188\/16.CJON.S1.2-8","volume":"20","author":"ME Davis","year":"2016","unstructured":"Davis ME. Glioblastoma: overview of disease and treatment. Clin J Oncol Nurs. 2016;20(5 Suppl):S2\u20138.","journal-title":"Clin J Oncol Nurs"},{"issue":"3","key":"4794_CR3","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3322\/caac.20069","volume":"60","author":"EG Van Meir","year":"2010","unstructured":"Van Meir EG, et al. Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J Clin. 2010;60(3):166\u201393.","journal-title":"CA Cancer J Clin"},{"issue":"6","key":"4794_CR4","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1053\/j.semnuclmed.2012.06.001","volume":"42","author":"K Herholz","year":"2012","unstructured":"Herholz K, et al. Brain tumors. Semin Nucl Med. 2012;42(6):356\u201370.","journal-title":"Semin Nucl Med"},{"issue":"7","key":"4794_CR5","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1093\/neuonc\/nou087","volume":"16","author":"QT Ostrom","year":"2014","unstructured":"Ostrom QT, et al. The epidemiology of glioma in adults: a \u201cstate of the science\u201d review. Neuro Oncol. 2014;16(7):896\u2013913.","journal-title":"Neuro Oncol"},{"key":"4794_CR6","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","volume":"102","author":"A I\u015f\u0131n","year":"2016","unstructured":"I\u015f\u0131n A, Direko\u011flu C, \u015eah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput Sci. 2016;102:317\u201324.","journal-title":"Procedia Comput Sci"},{"key":"4794_CR7","doi-asserted-by":"publisher","first-page":"6695108","DOI":"10.1155\/2021\/6695108","volume":"2021","author":"SR Gunasekara","year":"2021","unstructured":"Gunasekara SR, Kaldera HNTK, Dissanayake MB. A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. J Healthc Eng. 2021;2021:6695108.","journal-title":"J Healthc Eng"},{"key":"4794_CR8","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao X, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal. 2018;43:98\u2013111.","journal-title":"Med Image Anal"},{"key":"4794_CR9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18\u201331.","journal-title":"Med Image Anal"},{"key":"4794_CR10","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, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61\u201378.","journal-title":"Med Image Anal"},{"issue":"6","key":"4794_CR11","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s11548-020-02186-z","volume":"15","author":"RA Zeineldin","year":"2020","unstructured":"Zeineldin RA, et al. DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. Int J Comput Assist Radiol Surg. 2020;15(6):909\u201320.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4794_CR12","doi-asserted-by":"crossref","unstructured":"Alkassar S, Abdullah MAM, Jebur BA. Automatic brain tumour segmentation using fully convolution network and transfer learning. In: 2019 2nd international conference on electrical, communication, computer, power and control engineering (ICECCPCE). 2019.","DOI":"10.1109\/ICECCPCE46549.2019.203771"},{"key":"4794_CR13","doi-asserted-by":"crossref","unstructured":"Chahal ES, et al. Deep Learning Model for Brain Tumor Segmentation & Analysis. In: 2019 3rd International conference on recent developments in control, automation & power engineering (RDCAPE). 2019.","DOI":"10.1109\/RDCAPE47089.2019.8979076"},{"key":"4794_CR14","doi-asserted-by":"publisher","first-page":"152821","DOI":"10.1109\/ACCESS.2019.2948120","volume":"7","author":"Y Ding","year":"2019","unstructured":"Ding Y, et al. How to improve the deep residual network to segment multi-modal brain tumor images. IEEE Access. 2019;7:152821\u201331.","journal-title":"IEEE Access"},{"key":"4794_CR15","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez I, Mart\u00edn A, Schiavi E. Optimization of a variational model using deep learning: an application to brain tumor segmentation. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). 2018.","DOI":"10.1109\/ISBI.2018.8363654"},{"issue":"11","key":"4794_CR16","doi-asserted-by":"publisher","first-page":"9249","DOI":"10.1007\/s13369-019-03967-8","volume":"44","author":"S Sajid","year":"2019","unstructured":"Sajid S, Hussain S, Sarwar A. Brain tumor detection and segmentation in MR images using deep learning. Arab J Sci Eng. 2019;44(11):9249\u201361.","journal-title":"Arab J Sci Eng"},{"key":"4794_CR17","doi-asserted-by":"crossref","unstructured":"Wang Y, et al. A Deep learning algorithm for fully automatic brain tumor segmentation. In: 2019 international joint conference on neural networks (IJCNN). 2019.","DOI":"10.1109\/IJCNN.2019.8852210"},{"key":"4794_CR18","doi-asserted-by":"crossref","unstructured":"Jiang Z, et al. Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. 2020. p. 231\u201341.","DOI":"10.1007\/978-3-030-46640-4_22"},{"key":"4794_CR19","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-030-46640-4_20","volume-title":"Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries","author":"Y-X Zhao","year":"2020","unstructured":"Zhao Y-X, Zhang Y-M, Liu C-L. Bag of tricks for 3D MRI brain tumor segmentation. In: Crimi A, Bakas S, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer International Publishing; 2020. p. 210\u201320. https:\/\/doi.org\/10.1007\/978-3-030-46640-4_20."},{"issue":"2","key":"4794_CR20","doi-asserted-by":"publisher","first-page":"186","DOI":"10.18383\/j.tom.2019.00026","volume":"6","author":"CGB Yogananda","year":"2020","unstructured":"Yogananda CGB, et al. A fully automated deep learning network for brain tumor segmentation. Tomography. 2020;6(2):186\u201393.","journal-title":"Tomography"},{"key":"4794_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/fradi.2021.704888","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Zhong P, Jie D, Jiewei W, Zeng S, Chu J, Yilong Liu E, Tang X. Brain tumor segmentation from multi-modal MR images via ensembling UNets. Front Radiol. 2021. https:\/\/doi.org\/10.3389\/fradi.2021.704888.","journal-title":"Front Radiol"},{"issue":"2","key":"4794_CR22","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3390\/jimaging7020019","volume":"7","author":"T Magadza","year":"2021","unstructured":"Magadza T, Viriri S. Deep learning for brain tumor segmentation: a survey of state-of-the-art. J Imaging. 2021;7(2):19. https:\/\/doi.org\/10.3390\/jimaging7020019.","journal-title":"J Imaging"},{"key":"4794_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0561-x","author":"R Chauhan","year":"2017","unstructured":"Chauhan R, Kaur H, Chang V. Advancement and applicability of classifiers for variant exponential model to optimize the accuracy for deep learning. J Ambient Intell Human Comput. 2017. https:\/\/doi.org\/10.1007\/s12652-017-0561-x.","journal-title":"J Ambient Intell Human Comput"},{"key":"4794_CR24","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.patrec.2018.01.021","volume":"139","author":"M Sharif","year":"2020","unstructured":"Sharif M, et al. A framework for offline signature verification system: best features selection approach. Pattern Recogn Lett. 2020;139:50\u20139.","journal-title":"Pattern Recogn Lett"},{"key":"4794_CR25","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, et al. Recent advances in convolutional neural networks. Pattern Recognit. 2018;77:354\u201377.","journal-title":"Pattern Recognit"},{"issue":"7639","key":"4794_CR26","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115\u20138.","journal-title":"Nature"},{"issue":"22","key":"4794_CR27","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402\u201310.","journal-title":"JAMA"},{"key":"4794_CR28","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. 2016. Springer.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"4794_CR29","doi-asserted-by":"crossref","unstructured":"Wang, G., et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In International MICCAI brainlesion workshop, Springer; 2017.","DOI":"10.1007\/978-3-319-75238-9_16"},{"key":"4794_CR30","doi-asserted-by":"crossref","unstructured":"Sun L, Zhang S, Luo L. Tumor segmentation and survival prediction in glioma with deep learning. In: International MICCAI Brainlesion workshop, Springer; 2018.","DOI":"10.1007\/978-3-030-11726-9_8"},{"key":"4794_CR31","doi-asserted-by":"crossref","unstructured":"Fang J, et al. Cloud Computing: Virtual Web Hosting on Infrastructure as a Service (IaaS). in International Conference on Mobile Ad-Hoc and Sensor Networks, Springer; 2017.","DOI":"10.1007\/978-981-10-8890-2_34"},{"key":"4794_CR32","doi-asserted-by":"crossref","unstructured":"Shaukat Z, et al. Facial recognition on cloud for android based wearable devices. In: International conference on applied human factors and ergonomics. Springer; 2019.","DOI":"10.1007\/978-3-030-20476-1_12"},{"key":"4794_CR33","doi-asserted-by":"crossref","unstructured":"Shaukat Z, et al. Cloud based face recognition for google glass. In: Proceedings of the 2018 International conference on computing and artificial intelligence. 2018. ACM.","DOI":"10.1145\/3194452.3194479"},{"issue":"39","key":"4794_CR34","doi-asserted-by":"publisher","first-page":"29537","DOI":"10.1007\/s11042-020-09494-1","volume":"79","author":"Z Shaukat","year":"2020","unstructured":"Shaukat Z, et al. Cloud-based efficient scheme for handwritten digit recognition. Multimed Tools Appl. 2020;79(39):29537\u201349.","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"4794_CR35","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1016\/j.mri.2013.05.002","volume":"31","author":"N Gordillo","year":"2013","unstructured":"Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging. 2013;31(8):1426\u201338.","journal-title":"Magn Reson Imaging"},{"issue":"11","key":"4794_CR36","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1109\/TMI.2018.2835303","volume":"37","author":"L Chen","year":"2018","unstructured":"Chen L, et al. DRINet for medical image segmentation. IEEE Trans Med Imaging. 2018;37(11):2453\u201362.","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"4794_CR37","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993\u20132024.","journal-title":"IEEE Trans Med Imaging"},{"key":"4794_CR38","first-page":"269","volume":"1","author":"MG Linguraru","year":"2007","unstructured":"Linguraru MG, et al. Segmentation propagation from deformable atlases for brain mapping and analysis. Brain Res J. 2007;1:269.","journal-title":"Brain Res J."},{"key":"4794_CR39","unstructured":"Cocosco CA, et al. BrainWeb: online interface to a 3d mri simulated brain database, 1997."},{"issue":"6","key":"4794_CR40","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, et al. The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045\u201357.","journal-title":"J Digit Imaging"},{"key":"4794_CR41","unstructured":"Antonelli M, et al. The medical segmentation decathlon, 2021."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04794-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04794-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04794-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T11:08:47Z","timestamp":1656068927000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04794-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["4794"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04794-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,24]]},"assertion":[{"value":"31 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 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":"All methods were carried out in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"251"}}