{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T14:19:46Z","timestamp":1767968386374,"version":"3.49.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFC0407904"],"award-info":[{"award-number":["2018YFC0407904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51667017"],"award-info":[{"award-number":["51667017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research Projects of Tibet Autonomous Region for Innovation and Entrepreneur","award":["Z2016D01G01\/01"],"award-info":[{"award-number":["Z2016D01G01\/01"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Smart hospitals are important components of smart cities. An intelligent medical system for brain tumor segmentation is required to construct smart hospitals. To achieve intelligent brain tumor segmentation, morphological variety and serious category imbalance must be managed effectively. Conventional deep neural networks have difficulty in predicting high-accuracy segmentation images due to these issues. To solve these problems, we propose using multimodal brain tumor images combined with the UNET and LSTM models to construct a new network structure with a mixed loss function to solve sample imbalance and describe an intelligent segmentation process to identify brain tumors. To verify the practicability of this algorithm, we used the open source Brain Tumor Segmentation Challenge dataset to train and verify the proposed network. We obtained DSCs of 0.91, 0.82, and 0.80; sensitivities of 0.93, 0.85, and 0.82; and specificities of 0.99, 0.99, and 0.98 in three tumor regions, including the\n            <jats:bold>whole tumor<\/jats:bold>\n            (\n            <jats:bold>WT<\/jats:bold>\n            ),\n            <jats:bold>tumor core<\/jats:bold>\n            (\n            <jats:bold>TC<\/jats:bold>\n            ), and\n            <jats:bold>enhanced<\/jats:bold>\n            <jats:bold>tumor<\/jats:bold>\n            (\n            <jats:bold>ET<\/jats:bold>\n            ). We also compared the results of the proposed network with those of other brain tumor segmentation methods, and the results showed that the proposed algorithm could segment different tumor lesions more accurately, highlighting its potential application value in the clinical diagnosis of brain tumors.\n          <\/jats:p>","DOI":"10.1145\/3450519","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T14:51:35Z","timestamp":1623855095000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Multimodal Brain Tumor Segmentation Based on an Intelligent UNET-LSTM Algorithm in Smart Hospitals"],"prefix":"10.1145","volume":"21","author":[{"given":"He-Xuan","family":"Hu","sequence":"first","affiliation":[{"name":"Hohai University, and Tibet Agriculture &amp; Animal Husbandry University, People's Republic of China"}]},{"given":"Wen-Jie","family":"Mao","sequence":"additional","affiliation":[{"name":"Hohai University, People's Republic of China"}]},{"given":"Zhen-Zhou","family":"Lin","sequence":"additional","affiliation":[{"name":"Nanjing University of Finance and Economics, People's Republic of China"}]},{"given":"Qiang","family":"Hu","sequence":"additional","affiliation":[{"name":"Hohai University, People's Republic of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6093-4348","authenticated-orcid":false,"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hohai University, People's Republic of China"}]}],"member":"320","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.06.029"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","first-page":"111011","DOI":"10.3788\/LOP55.111011","article-title":"Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow","volume":"55","author":"Lu R.","year":"2019","unstructured":"R. Lu , L. Qiang , G. Xin , 2019 . Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow . Laser & Optoelectronics Progress 55 , 11 (2019), 111011 . R. Lu, L. Qiang, G. Xin, et al. 2019. Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow. Laser & Optoelectronics Progress 55, 11 (2019), 111011.","journal-title":"Laser & Optoelectronics Progress"},{"key":"e_1_2_1_3_1","volume-title":"2007 International Conference on Intelligent and Advanced Systems. IEEE, 422\u2013426","author":"Shanthi K. J.","unstructured":"K. J. Shanthi and M. S. Kumar . 2007. Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques . In 2007 International Conference on Intelligent and Advanced Systems. IEEE, 422\u2013426 . K. J. Shanthi and M. S. Kumar. 2007. Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques. In 2007 International Conference on Intelligent and Advanced Systems. IEEE, 422\u2013426."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-28962-0_22"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.218.2.r01fe44586"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings MICCAI-BRATS","author":"Zikic D.","year":"2014","unstructured":"D. Zikic , Y. Ioannou , M. Brown , 2014 . Segmentation of brain tumor tissues with convolutional neural networks . Proceedings MICCAI-BRATS (2014), 36\u201339. D. Zikic, Y. Ioannou, M. Brown, et al. 2014. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS (2014), 36\u201339."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2016.08.004"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2538465"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings MICCAI-BRATS","author":"Zikic D.","year":"2014","unstructured":"D. Zikic , Y. Ioannou , M. Brown , 2014 . Segmentation of brain tumor tissues with convolutional neural networks . Proceedings MICCAI-BRATS (2014), 36\u201339. D. Zikic, Y. Ioannou, M. Brown, et al. 2014. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS (2014), 36\u201339."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings MICCAI-BRATS Workshop. 4\u20139.","author":"Chang P. D.","year":"2016","unstructured":"P. D. Chang . 2016 . Fully convolutional neural networks with hyperlocal features for brain tumor segmentation . In Proceedings MICCAI-BRATS Workshop. 4\u20139. P. D. Chang. 2016. Fully convolutional neural networks with hyperlocal features for brain tumor segmentation. In Proceedings MICCAI-BRATS Workshop. 4\u20139."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"L. Zhao and K. Jia. 2016. Multiscale CNNs for brain tumor segmentation and diagnosis. Computational and Mathematical Methods in Medicine 2016 (2016).  L. Zhao and K. Jia. 2016. Multiscale CNNs for brain tumor segmentation and diagnosis. Computational and Mathematical Methods in Medicine 2016 (2016).","DOI":"10.1155\/2016\/8356294"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"e_1_2_1_13_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Ronneberger O.","year":"2015","unstructured":"O. Ronneberger , P. Fischer , and T. Brox . 2015 . U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer , Cham , 234\u2013241. O. Ronneberger, P. Fischer, and T. Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 234\u2013241."},{"key":"e_1_2_1_14_1","volume-title":"European Conference on Computer Vision. Springer, Cham, 818\u2013833","author":"Zeiler M. D.","unstructured":"M. D. Zeiler , and R. Fergus . 2014. Visualizing and understanding convolutional networks . In European Conference on Computer Vision. Springer, Cham, 818\u2013833 . M. D. Zeiler, and R. Fergus. 2014. Visualizing and understanding convolutional networks. In European Conference on Computer Vision. Springer, Cham, 818\u2013833."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.05.001"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363653"},{"key":"e_1_2_1_17_1","volume-title":"A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS One 12, 7","author":"Bao W.","year":"2017","unstructured":"W. Bao , J. Yue , and Y. Rao . A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS One 12, 7 ( 2017 ), e0180944. W. Bao, J. Yue, and Y. Rao. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS One 12, 7 (2017), e0180944."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244303768966139"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/870468"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/3153803"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117"},{"key":"e_1_2_1_23_1","unstructured":"S. Bakas M. Reyes A. Jakab etal 2018. Identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the BRATS challenge. https:\/\/arxiv.org\/abs\/1811.02629 Retrived November 2018.  S. Bakas M. Reyes A. Jakab et al. 2018. Identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the BRATS challenge. https:\/\/arxiv.org\/abs\/1811.02629 Retrived November 2018."},{"key":"e_1_2_1_24_1","volume-title":"CNN-based segmentation of medical imaging data. https:\/\/arxiv.org\/abs\/1701.03056 Retrived","author":"Kayalibay B.","year":"2017","unstructured":"B. Kayalibay , G. Jensen , and P. van der Smagt . 2017. CNN-based segmentation of medical imaging data. https:\/\/arxiv.org\/abs\/1701.03056 Retrived January , 2017 . B. Kayalibay, G. Jensen, and P. van der Smagt. 2017. CNN-based segmentation of medical imaging data. https:\/\/arxiv.org\/abs\/1701.03056 Retrived January, 2017."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.004"},{"key":"e_1_2_1_26_1","doi-asserted-by":"crossref","unstructured":"C. Zhang X. Shen H. Cheng etal 2019. Brain tumor segmentation based on hybrid clustering and morphological operations. International Journal of Biomedical Imaging 2019 (2019).  C. Zhang X. Shen H. Cheng et al. 2019. Brain tumor segmentation based on hybrid clustering and morphological operations. International Journal of Biomedical Imaging 2019 (2019).","DOI":"10.1155\/2019\/7305832"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3788\/AOS202040.0610001"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450519","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:49Z","timestamp":1750193269000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,16]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3450519"],"URL":"https:\/\/doi.org\/10.1145\/3450519","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,16]]},"assertion":[{"value":"2020-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}