{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:49:39Z","timestamp":1743007779032,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031090011"},{"type":"electronic","value":"9783031090028"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-09002-8_19","type":"book-chapter","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T12:05:34Z","timestamp":1657800334000},"page":"210-221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Based Ensemble Approach for\u00a03D MRI Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Tien-Bach-Thanh","family":"Do","sequence":"first","affiliation":[]},{"given":"Dang-Linh","family":"Trinh","sequence":"additional","affiliation":[]},{"given":"Minh-Trieu","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Guee-Sang","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Soo-Hyung","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hyung-Jeong","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"issue":"2017","key":"19_CR1","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. 35(2017), 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"19_CR2","series-title":"Lecture Notes in Computer Science","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.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"19_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"19_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/978-3-030-11726-9_22","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"N Nuechterlein","year":"2019","unstructured":"Nuechterlein, N., Mehta, S.: 3D-ESPNet with pyramidal refinement for volumetric brain tumor image segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 245\u2013253. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_22"},{"key":"19_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-030-11726-9_32","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"W Chen","year":"2019","unstructured":"Chen, W., Liu, B., Peng, S., Sun, J., Qiao, X.: S3D-UNet: separable 3D U-Net for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 358\u2013368. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_32"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Kuang, Z., Li, Z., Zhao, T., Fan, J.: Deep multi-task learning for large-scale image classification. In: 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), pp. 310\u2013317. IEEE (2017)","DOI":"10.1109\/BigMM.2017.72"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Li, L., Gong, B.: End-to-end video captioning with multitask reinforcement learning. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 339\u2013348. IEEE (2019)","DOI":"10.1109\/WACV.2019.00042"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Lee, G.W., Kim, H.K.: Multi-task learning U-Net for single-channel speech enhancement and mask-based voice activity detection. Appl. Sci. 10(9), 3230 (2020)","DOI":"10.3390\/app10093230"},{"key":"19_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101666","volume":"61","author":"T He","year":"2020","unstructured":"He, T., Hu, J., Song, Y., Guo, J., Yi, Z.: Multi-task learning for the segmentation of organs at risk with label dependence. Med. Image Anal. 61, 101666 (2020)","journal-title":"Med. Image Anal."},{"key":"19_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-030-32692-0_18","volume-title":"Machine Learning in Medical Imaging","author":"A-A-Z Imran","year":"2019","unstructured":"Imran, A.-A.-Z., Terzopoulos, D.: Semi-supervised multi-task learning with chest X-ray images. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 151\u2013159. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32692-0_18"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, C., Ding, M., Li, J., Yu, H., Zha, S.: TransBTS: multimodal brain tumor segmentation using transformer. arXiv preprint arXiv:2103.04430 (2021)","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"19_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-319-66185-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"H Shen","year":"2017","unstructured":"Shen, H., Wang, R., Zhang, J., McKenna, S.J.: Boundary-aware fully convolutional network for brain tumor segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 433\u2013441. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_49"},{"key":"19_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-030-46643-5_13","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"P Ribalta Lorenzo","year":"2020","unstructured":"Ribalta Lorenzo, P., Marcinkiewicz, M., Nalepa, J.: Multi-modal U-Nets with boundary loss and pre-training for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11993, pp. 135\u2013147. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46643-5_13"},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-030-32248-9_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"C Chen","year":"2019","unstructured":"Chen, C., Liu, X., Ding, M., Zheng, J., Li, J.: 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 184\u2013192. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_21"},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"7790","DOI":"10.3390\/app10217790","volume":"10","author":"D-K Ngo","year":"2020","unstructured":"Ngo, D.-K., Tran, M.-T., Kim, S.-H., Yang, H.-J., Lee, G.-S.: Multi-task learning for small brain tumor segmentation from MRI. Appl. Sci. 10, 7790 (2020). Multidisciplinary Digital Publishing Institute","journal-title":"Appl. Sci."},{"issue":"10","key":"19_CR17","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017). https:\/\/doi.org\/10.1038\/sdata.2017.117","journal-title":"Nat. Sci. Data"},{"key":"19_CR19","unstructured":"Bakas, S., et al.: 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 (2018)"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Luo, Z., Jia, Z., Yuan, Z., Peng, J.: HDC-Net: hierarchical decoupled convolution network for brain tumor segmentation, pp. 737\u2013745. IEEE (2020)","DOI":"10.1109\/JBHI.2020.2998146"},{"key":"19_CR21","unstructured":"Baid, U., Ghodasara, S., Mohan, S., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv:2107.02314 (2021)"},{"key":"19_CR22","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","journal-title":"Cancer Imaging Arch."},{"key":"19_CR23","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.GJQ7R0EF","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF","journal-title":"Cancer Imaging Arch."}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09002-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T12:08:30Z","timestamp":1657800510000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09002-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031090011","9783031090028"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09002-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.brainlesion-workshop.org\/?msclkid=7759e32ed14111ecba82c5ba435279db","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"151","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}