{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:53:36Z","timestamp":1774403616112,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030720865","type":"print"},{"value":"9783030720872","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72087-2_14","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T04:09:34Z","timestamp":1616645374000},"page":"158-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Deep Supervision CNN Network for Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Shiqiang","family":"Ma","sequence":"first","affiliation":[]},{"given":"Zehua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiaqi","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Xuejian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-030-46640-4_36","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"R McKinley","year":"2020","unstructured":"McKinley, R., Rebsamen, M., Meier, R., Wiest, R.: Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 379\u2013387. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_36"},{"key":"14_CR2","series-title":"Lecture Notes in Computer Science","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. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 210\u2013220. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_20"},{"key":"14_CR3","unstructured":"Zhou, C., Chen, S., Ding, C., Tao, D.: Learning contexualand attentive information for brain tumor segmentation. In: Pre-conference Proceedings of the 2018 7th MICCAI BraTS Challenge, pp. 571\u2013578 (2018)"},{"issue":"10","key":"14_CR4","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., 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":"14_CR5","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, J.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":"14_CR6","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., 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":"14_CR7","doi-asserted-by":"publisher","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF","DOI":"10.7937\/K9\/TCIA.2017.GJQ7R0EF"},{"key":"14_CR9","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 \u2014 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":"14_CR10","doi-asserted-by":"crossref","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maierhein, K.H.: Brain tumor segmentation and radiomics survival prediction: Contribution to the BRATS 2017 challenge. In: 2017 Proceedings of the 6th MICCAI BraTS Challenge, pp. 100\u2013107 (2017)","DOI":"10.1007\/978-3-319-75238-9_25"},{"key":"14_CR11","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maierhein, K.H.: No new-net. In: 2018 Pre-conference Proceedings of the 7th MICCAI BraTS Challenge, pp. 222\u2013231 (2018)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, Z.W., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.M.: UNet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Anylysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"14_CR15","unstructured":"Zhang, X., Jian, W., Cheng, K.: 3D dense U-nets for brain tumor segmentation. In: 2018 Pre-conference Proceedings of the 7th MICCAI BraTS Challenge, pp. 562\u2013570 (2018)"},{"key":"14_CR16","unstructured":"Stawiaski, J.: Leveraging a DenseNet encoder pre-trained on ImageNet for brain tumor segmentation. In: 2018 Pre-conference Proceedings of the 7th MICCAI BraTS Challenge, pp. 438\u2013447 (2018)"},{"key":"14_CR17","unstructured":"Mckinley, R., Meier, R., Wiest, R.: Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In: 2018 Pre-conference Proceedings 7th MICCAI BraTS Challenge, pp. 322\u2013330 (2018)"},{"key":"14_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-11726-9_28","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Myronenko","year":"2019","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311\u2013320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_28"},{"key":"14_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-030-46640-4_31","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"L Weninger","year":"2020","unstructured":"Weninger, L., Liu, Q., Merhof, D.: Multi-task learning for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 327\u2013337. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_31"},{"key":"14_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/978-3-030-00931-1_73","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"C Zhou","year":"2018","unstructured":"Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 637\u2013645. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_73"},{"key":"14_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-32248-9_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Chen","year":"2019","unstructured":"Chen, S., Bortsova, G., Garc\u00eda-Uceda Ju\u00e1rez, A., van Tulder, G., de Bruijne, M.: Multi-task attention-based semi-supervised learning for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 457\u2013465. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_51"},{"key":"14_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-46640-4_22","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 231\u2013241. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_22"},{"key":"14_CR23","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 4th International Conference on 3D Vision (3DV). IEEE (2016)","DOI":"10.1109\/3DV.2016.79"}],"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\/978-3-030-72087-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:04:35Z","timestamp":1774400675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72087-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720865","9783030720872"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72087-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}