{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T23:13:37Z","timestamp":1773616417859,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Cancer Institute","award":["R44CA254844"],"award-info":[{"award-number":["R44CA254844"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"DOI":"10.1007\/s10278-023-00860-7","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T21:01:21Z","timestamp":1687294881000},"page":"2075-2087","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Brain Tumor Segmentation for Multi-Modal MRI with Missing Information"],"prefix":"10.1007","volume":"36","author":[{"given":"Xue","family":"Feng","sequence":"first","affiliation":[]},{"given":"Kanchan","family":"Ghimire","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0731-8559","authenticated-orcid":false,"given":"Daniel D.","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Rajat S.","family":"Chandra","sequence":"additional","affiliation":[]},{"given":"Helen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Binghong","family":"Han","sequence":"additional","affiliation":[]},{"given":"Gaofeng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Quan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Sohil","family":"Patel","sequence":"additional","affiliation":[]},{"given":"Chetan","family":"Bettagowda","sequence":"additional","affiliation":[]},{"given":"Haris I.","family":"Sair","sequence":"additional","affiliation":[]},{"given":"Craig","family":"Jones","sequence":"additional","affiliation":[]},{"given":"Zhicheng","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Harrison","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"860_CR1","doi-asserted-by":"publisher","unstructured":"Yang Y, Zhuang Y, Pan Y. Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies. Frontiers of Information Technology & Electronic Engineering. 2021\/12\/01 2021;22(12):1551\u20131558. https:\/\/doi.org\/10.1631\/FITEE.2100463","DOI":"10.1631\/FITEE.2100463"},{"key":"860_CR2","doi-asserted-by":"publisher","first-page":"232","DOI":"10.3389\/fneur.2017.00232","volume":"8","author":"I Havsteen","year":"2017","unstructured":"Havsteen I, Ohlhues A, Madsen KH, Nybing JD, Christensen H, Christensen A. Are Movement Artifacts in Magnetic Resonance Imaging a Real Problem?-A Narrative Review. Front Neurol. 2017;8:232. https:\/\/doi.org\/10.3389\/fneur.2017.00232","journal-title":"Front Neurol."},{"key":"860_CR3","unstructured":"Azad R, Khosravi N, Dehghanmanshadi M, Cohen-Adad J, Merhof D. Medical image segmentation on mri images with missing modalities: A review. arXiv preprint\u00a0https:\/\/arxiv.org\/abs\/2203.06217. 2022"},{"key":"860_CR4","doi-asserted-by":"crossref","unstructured":"Havaei M, Guizard N, Chapados N, Bengio Y. Hemis: Hetero-modal image segmentation. Springer; 2016:469\u2013477","DOI":"10.1007\/978-3-319-46723-8_54"},{"key":"860_CR5","doi-asserted-by":"crossref","unstructured":"Dorent R, Joutard S, Modat M, Ourselin S, Vercauteren T. Hetero-modal variational encoder-decoder for joint modality completion and segmentation. Springer; 2019:74\u201382","DOI":"10.1007\/978-3-030-32245-8_9"},{"key":"860_CR6","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang Y, Liu Y, et al. Acn: Adversarial co-training network for brain tumor segmentation with missing modalities. Springer; 2021:410\u2013420","DOI":"10.1007\/978-3-030-87234-2_39"},{"key":"860_CR7","doi-asserted-by":"publisher","unstructured":"Wang Q, Zhan L, Thompson P, Zhou J. Multimodal Learning with Incomplete Modalities by Knowledge Distillation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020:1828\u20131838. https:\/\/doi.org\/10.1145\/3394486.3403234","DOI":"10.1145\/3394486.3403234"},{"key":"860_CR8","unstructured":"Zhou T, Canu S, Vera P, Ruan S. Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities. arXiv preprint\u00a0https:\/\/arxiv.org\/abs\/2105.13013. 2021"},{"key":"860_CR9","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1109\/TIP.2021.3070752","volume":"30","author":"T Zhou","year":"2021","unstructured":"Zhou T, Canu S, Vera P, Ruan S. Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities. IEEE Transactions on Image Processing. 2021;30:4263-4274. https:\/\/doi.org\/10.1109\/TIP.2021.3070752","journal-title":"IEEE Transactions on Image Processing."},{"key":"860_CR10","doi-asserted-by":"crossref","unstructured":"Chen C, Dou Q, Jin Y, Chen H, Qin J, Heng P-A. Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion. Springer International Publishing; 2019:447\u2013456","DOI":"10.1007\/978-3-030-32248-9_50"},{"issue":"4","key":"860_CR11","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TMI.2019.2945521","volume":"39","author":"A Sharma","year":"2020","unstructured":"Sharma A, Hamarneh G. Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network. IEEE Transactions on Medical Imaging. 2020;39(4):1170-1183. https:\/\/doi.org\/10.1109\/TMI.2019.2945521","journal-title":"IEEE Transactions on Medical Imaging."},{"key":"860_CR12","unstructured":"Gatys LA, Ecker AS, Bethge M. A neural algorithm of artistic style. arXiv preprint\u00a0https:\/\/arxiv.org\/abs\/1508.06576. 2015"},{"key":"860_CR13","unstructured":"Azad R, Khosravi N, Merhof D. SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities. Medical Imaging with Deep Learning. 2022"},{"key":"860_CR14","doi-asserted-by":"publisher","first-page":"3955","DOI":"10.1109\/ICCV48922.2021.00394","volume":"2021","author":"Y Ding","year":"2021","unstructured":"Ding Y, Yu X, Yang Y. RFNet: Region-aware Fusion Network for Incomplete Multi-modal Brain Tumor Segmentation. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). 2021:3955-3964","journal-title":"IEEE\/CVF International Conference on Computer Vision (ICCV)."},{"key":"860_CR15","unstructured":"Baid U, Ghodasara S, Mohan S, et al. The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint\u00a0https:\/\/arxiv.org\/abs\/2107.02314. 2021"},{"key":"860_CR16","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015:\u00a0http:\/\/arxiv.org\/abs\/1505.04597. Accessed May 01, 2015. https:\/\/ui.adsabs.harvard.edu\/abs\/2015arXiv150504597R"},{"key":"860_CR17","doi-asserted-by":"publisher","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2021\/02\/01 2021;18(2):203\u2013211. https:\/\/doi.org\/10.1038\/s41592-020-01008-z","DOI":"10.1038\/s41592-020-01008-z"},{"key":"860_CR18","doi-asserted-by":"publisher","unstructured":"Bakas S, Akbari H, Sotiras A, et al. Data from: Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection. 2017. The Cancer Imaging Archive. https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"key":"860_CR19","unstructured":"Gal Y, Ghahramani Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. 2015:\u00a0http:\/\/arxiv.org\/abs\/1506.02142. Accessed June 01, 2015. https:\/\/ui.adsabs.harvard.edu\/abs\/2015arXiv150602142G"},{"issue":"6","key":"860_CR20","doi-asserted-by":"publisher","first-page":"e270","DOI":"10.1016\/s1470-2045(15)70057","volume":"16","author":"NU Lin","year":"2015","unstructured":"Lin NU, Lee EQ, Aoyama H, et al. Response assessment criteria for brain metastases: proposal from the RANO group. Lancet Oncol. Jun 2015;16(6):e270\u20138.\u00a0https:\/\/doi.org\/10.1016\/s1470-2045(15)70057","journal-title":"Lancet Oncol."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00860-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00860-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00860-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T18:05:29Z","timestamp":1694714729000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00860-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,20]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["860"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00860-7","relation":{},"ISSN":["1618-727X"],"issn-type":[{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,20]]},"assertion":[{"value":"21 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study used a public dataset of MRI images, and informed consent was not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This study did not use human subjects. It used a public dataset of MRI images.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"This study did not use individual person\u2019s data.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"RSC reports personal fees from Roivant Sciences, personal fees from Sumitovant Biopharma, outside the submitted work. XF, KG, GH, and QC are employees of Carina Medical LLC. The rest of the authors declare no conflicts of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}