{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:57:57Z","timestamp":1774490277857,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030720834","type":"print"},{"value":"9783030720841","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\/978-3-030-72084-1_40","type":"book-chapter","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T07:03:03Z","timestamp":1616742183000},"page":"448-457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Gowtham Krishnan","family":"Murugesan","sequence":"first","affiliation":[]},{"given":"Sahil","family":"Nalawade","sequence":"additional","affiliation":[]},{"given":"Chandan","family":"Ganesh","sequence":"additional","affiliation":[]},{"given":"Ben","family":"Wagner","sequence":"additional","affiliation":[]},{"given":"Fang F.","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Baowei","family":"Fei","sequence":"additional","affiliation":[]},{"given":"Ananth J.","family":"Madhuranthakam","sequence":"additional","affiliation":[]},{"given":"Joseph A.","family":"Maldjian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"40_CR1","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017a)"},{"key":"40_CR2","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive, 286 (2017b)."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features Sci. Data, 4, 170117 (2017c)","DOI":"10.1038\/sdata.2017.117"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data, 4, 170117 (2017d)","DOI":"10.1038\/sdata.2017.117"},{"key":"40_CR5","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 (2018)"},{"key":"40_CR6","unstructured":"Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project (2013)"},{"key":"40_CR7","unstructured":"Chen, C.F., Fan, Q., Mallinar, N., Sercu, T., Feris, R.: Big-little net: an efficient multi-scale feature representation for visual and speech recognition (2018)"},{"key":"40_CR8","unstructured":"Chen, L., Wu, Y., DSouza, A.M., Abidin, A.Z., Wism\u00fcller, A., Xu, C.:. MRI tumor segmentation with densely connected 3D CNN. In: Medical Imaging 2018: Image Processing. International Society for Optics and Photonics (2018)"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","volume":"38","author":"J Dolz","year":"2018","unstructured":"Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ayed, I.B.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans. Med. Imaging 38(5), 1116\u20131126 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"40_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/978-3-030-11726-9_25","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"X Feng","year":"2019","unstructured":"Feng, X., Tustison, N., Meyer, C.: Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 279\u2013288. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_25"},{"key":"40_CR12","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, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"40_CR15","unstructured":"Islam, J., Zhang, Y.: An ensemble of deep convolutional neural networks for Alzheimer's disease detection and classification (2017)"},{"issue":"6","key":"40_CR16","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1007\/s00259-018-3948-9","volume":"45","author":"S Kim","year":"2018","unstructured":"Kim, S., Kim, D., Kim, S.H., Park, M., Chang, J.H., Yun, M.: The roles of 11Ccetate PET\/CT in predicting tumor differentiation and survival in patients with cerebral glioma. Eur. J. Nucl. Med. Mol. Imaging 45(6), 1012\u20131020 (2018). https:\/\/doi.org\/10.1007\/s00259-018-3948-9","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"36","key":"40_CR17","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.21105\/joss.01237","volume":"4","author":"GR Lee","year":"2019","unstructured":"Lee, G.R., Gommers, R., Waselewski, F., Wohlfahrt, K., O\u2019Leary, A.: PyWavelets: a python package for wavelet analysis. J. Open Source Softw. 4(36), 1237 (2019)","journal-title":"J. Open Source Softw."},{"key":"40_CR18","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400"},{"issue":"10","key":"40_CR19","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"13","key":"40_CR20","doi-asserted-by":"publisher","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","volume":"115","author":"P Mobadersany","year":"2018","unstructured":"Mobadersany, P., et al.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci. 115(13), E2970\u2013E2979 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"40_CR21","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"},{"issue":"Oct","key":"40_CR22","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"5","key":"40_CR23","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240\u20131251 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"40_CR24","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1002\/hbm.20906","volume":"31","author":"T Rohlfing","year":"2010","unstructured":"Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Human Brain Mapp. 31(5), 798\u2013819 (2010)","journal-title":"Human Brain Mapp."},{"key":"40_CR25","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: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III","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.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III, pp. 234\u2013241. Springer International Publishing, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"40_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.cmpb.2018.09.007","volume":"166","author":"R Saouli","year":"2018","unstructured":"Saouli, R., Akil, M., Kachouri, R.: Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Comput. Methods Programs Biomed. 166, 39\u201349 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"40_CR27","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.nucmedbio.2007.11.004","volume":"35","author":"T Tsuchida","year":"2008","unstructured":"Tsuchida, T., Takeuchi, H., Okazawa, H., Tsujikawa, T., Fujibayashi, Y.: Grading of brain glioma with 1\u201311C-acetate PET: comparison with 18F-FDG PET. Nucl. Med. Biol. 35(2), 171\u2013176 (2008)","journal-title":"Nucl. Med. Biol."},{"key":"40_CR28","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2014.05.044","volume":"99","author":"NJ Tustison","year":"2014","unstructured":"Tustison, N.J., et al.: Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 99, 166\u2013179 (2014)","journal-title":"Neuroimage"},{"issue":"21","key":"40_CR29","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104\u2013e107 (2017)","journal-title":"Cancer Res."},{"key":"40_CR30","unstructured":"Yakubovskiy, P.: Segmentation models. GitHub repository (2019)"},{"issue":"5","key":"40_CR31","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s11307-008-0152-5","volume":"10","author":"YY Yamamoto","year":"2008","unstructured":"Yamamoto, Y.Y., et al.: 11 C-acetate PET in the evaluation of brain glioma: comparison with 11 C-methionine and 18 F-FDG-PET. Mol. Imaging Biol. 10(5), 281 (2008)","journal-title":"Mol. Imaging Biol."},{"issue":"10","key":"40_CR32","doi-asserted-by":"publisher","first-page":"5234","DOI":"10.1002\/mp.12481","volume":"44","author":"Y Zhuge","year":"2017","unstructured":"Zhuge, Y., et al.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44(10), 5234\u20135243 (2017)","journal-title":"Med. Phys."}],"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\/978-3-030-72084-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:04:39Z","timestamp":1774487079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72084-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720834","9783030720841"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72084-1_40","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":"27 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"}]}}