{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:52:19Z","timestamp":1774403539727,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"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_13","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T04:09:34Z","timestamp":1616645374000},"page":"148-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing"],"prefix":"10.1007","author":[{"given":"Vladimir","family":"Groza","sequence":"first","affiliation":[]},{"given":"Bair","family":"Tuchinov","sequence":"additional","affiliation":[]},{"given":"Evgeniya","family":"Amelina","sequence":"additional","affiliation":[]},{"given":"Evgeniy","family":"Pavlovskiy","sequence":"additional","affiliation":[]},{"given":"Nikolay","family":"Tolstokulakov","sequence":"additional","affiliation":[]},{"given":"Mikhail","family":"Amelin","sequence":"additional","affiliation":[]},{"given":"Sergey","family":"Golushko","sequence":"additional","affiliation":[]},{"given":"Andrey","family":"Letyagin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"issue":"6","key":"13_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394\u2013424 (2018)","journal-title":"CA Cancer J. Clin."},{"issue":"5","key":"13_CR2","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1055\/s-0029-1220202","volume":"9","author":"M Bobinski","year":"2009","unstructured":"Bobinski, M., Greco, C.M., Schrot, R.J.: Giant intracranial medullary thyroid carcinoma metastasis presenting as apoplexy. Skull Base 9(5), 359\u2013362 (2009)","journal-title":"Skull Base"},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1055\/s-0032-1320032","volume":"75","author":"J Chrastina","year":"2012","unstructured":"Chrastina, J., Novak, Z., Riha, I., et al.: Diagnostic value of brain tumor neuroendoscopic biopsy and correlation with open tumor resection. J. Neurol. Surg. A Cent. Eur. Neurosurg. 75(2), 110\u2013115 (2012)","journal-title":"J. Neurol. Surg. A Cent. Eur. Neurosurg."},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1007\/978-3-319-75238-9_38","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"K Kamnitsas","year":"2018","unstructured":"Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450\u2013462. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_38"},{"issue":"9","key":"13_CR5","doi-asserted-by":"publisher","first-page":"4718","DOI":"10.1007\/s00330-018-5984-z","volume":"29","author":"C Li","year":"2019","unstructured":"Li, C., et al.: Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma. Eur. Radiol. 29(9), 4718\u20134729 (2019). https:\/\/doi.org\/10.1007\/s00330-018-5984-z","journal-title":"Eur. Radiol."},{"key":"13_CR6","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":"13_CR7","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation, CoRR, vol. abs\/1707.03718 (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Milletari, et al., : V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 4th International Conference on 3D Vision, pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 1, pp. 5987\u20135995 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"13_CR10","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks, CoRR abs\/1709.01507. arXiv:1709.01507 (2017)"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Ge, C., Gu, I.Y., Store Jakola, A., Yang, J.: Cross-modality augmentation of brain MR images using a novel pairwise generative adversarial network for enhanced glioma classification. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 559\u2013563 (2019)","DOI":"10.1109\/ICIP.2019.8803808"},{"key":"13_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-319-46723-8_54","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"M Havaei","year":"2016","unstructured":"Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: HeMIS: hetero-modal image segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 469\u2013477. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_54"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Varsavsky, T., Eaton-Rosen, Z., Sudre, C.H., Nachev, P., Cardoso, M.J.: PIMMS: permutation invariant multi-modal segmentation, CoRR, vol. abs\/1807.06537. arXiv:1807.06537 (2018)","DOI":"10.1007\/978-3-030-00889-5_23"},{"key":"13_CR14","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. arXiv:1907.11150 (July 2019)","DOI":"10.1007\/978-3-030-32245-8_9"},{"key":"13_CR15","unstructured":"Roy, A.G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel \u2018squeeze & excitation\u2019 blocks, CoRR abs\/1808.08127. arXiv:1808.08127 (2018)"},{"key":"13_CR16","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":"13_CR17","unstructured":"Buslaev, A., Parinov, A., Khvedchenya, E., Iglovikov, V., Kalinin, A.: Albumentations: fast and flexible image augmentations. arXiv:1809.06839 (2018)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Letyagin, A.Y., et al.: Artificial intelligence for imaging diagnostics in neurosurgery. In: 2019 International Multi-conference on Engineering, Computer and Information Sciences (SIBIRCON), pp. 336\u2013337. IEEE-Institute of Electrical and Electronics Engineers Inc. (2019)","DOI":"10.1109\/SIBIRCON48586.2019.8958201"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/978-3-030-45385-5_62","volume-title":"Bioinformatics and Biomedical Engineering","author":"V Groza","year":"2020","unstructured":"Groza, V., et al.: Data preprocessing via multi-sequences MRI mixture to improve brain tumor segmentation. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortu\u00f1o, F. (eds.) IWBBIO 2020. LNCS, vol. 12108, pp. 695\u2013704. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45385-5_62"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Tolstokulakov, N., et al.: Data preprocessing via compositions multi-channel mri images to improve brain tumor segmentation. In: IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, IA, USA, vol. 2020, pp. 1\u20134 (2020). https:\/\/doi.org\/10.1109\/ISBIWorkshops50223.2020.9153416","DOI":"10.1109\/ISBIWorkshops50223.2020.9153416"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Letyagin, A., et al.: Multi-class brain tumor segmentation via multi-sequences MRI mixture data preprocessing. In: Cognitive Sciences, Genomics and Bioinformatics (CSGB), Novosibirsk, Russia, vol. 2020, pp. 185\u2013189 (2020). https:\/\/doi.org\/10.1109\/CSGB51356.2020.9214645","DOI":"10.1109\/CSGB51356.2020.9214645"},{"key":"13_CR22","unstructured":"Yan, Q., et al.: COVID-19 chest CT image segmentation - a deep convolutional neural network solution. arXiv:2004.10987 (2020)"},{"key":"13_CR23","doi-asserted-by":"publisher","unstructured":"Groza, V., Kuzin, A.: Pneumothorax segmentation with effective conditioned post-processing in chest x-ray. In: IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, IA, USA, vol. 2020, pp. 1\u20134 (2020). https:\/\/doi.org\/10.1109\/ISBIWorkshops50223.2020.9153444","DOI":"10.1109\/ISBIWorkshops50223.2020.9153444"},{"issue":"10","key":"13_CR24","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":"13_CR25","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":"13_CR26","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":"13_CR27","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":"13_CR28","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":"13_CR29","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. arXiv preprint arXiv:1707.03718 (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"13_CR31","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)"},{"key":"13_CR32","unstructured":"Agarap, A.F.: Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375 (2018)"}],"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_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:03:22Z","timestamp":1774400602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72087-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720865","9783030720872"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72087-2_13","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"}]}}