{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:57:59Z","timestamp":1774490279459,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"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_23","type":"book-chapter","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T07:03:03Z","timestamp":1616742183000},"page":"252-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Efficient Embedding Network for 3D Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Hicham","family":"Messaoudi","sequence":"first","affiliation":[]},{"given":"Ahror","family":"Belaid","sequence":"additional","affiliation":[]},{"given":"Mohamed Lamine","family":"Allaoui","sequence":"additional","affiliation":[]},{"given":"Ahcene","family":"Zetout","sequence":"additional","affiliation":[]},{"given":"Mohand Said","family":"Allili","sequence":"additional","affiliation":[]},{"given":"Souhil","family":"Tliba","sequence":"additional","affiliation":[]},{"given":"Douraied","family":"Ben Salem","sequence":"additional","affiliation":[]},{"given":"Pierre-Henri","family":"Conze","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q","author":"S Bakas","year":"2017","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. Cancer Imaging Arch. (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","journal-title":"Cancer Imaging Arch."},{"key":"23_CR2","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.GJQ7R0EF","author":"S Bakas","year":"2017","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. Cancer Imaging Arch. (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF","journal-title":"Cancer Imaging Arch."},{"key":"23_CR3","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":"23_CR4","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)"},{"issue":"13","key":"23_CR5","doi-asserted-by":"publisher","first-page":"R97","DOI":"10.1088\/0031-9155\/58\/13\/R97","volume":"58","author":"S Bauer","year":"2013","unstructured":"Bauer, S., Wiest, R., Nolte, L.-P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97\u2013R129 (2013). https:\/\/doi.org\/10.1088\/0031-9155\/58\/13\/R97","journal-title":"Phys. Med. Biol."},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"101733","DOI":"10.1016\/j.compmedimag.2020.101733","volume":"83","author":"P-H Conze","year":"2020","unstructured":"Conze, P.-H., Brochard, S., Burdin, V., Sheehan, F.T., Pons, C.: Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders. Comput. Med. Imaging Graph. 83, 101733 (2020). https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101733","journal-title":"Comput. Med. Imaging Graph."},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Conze, P.-H., et al.: Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks. arXiv preprint arXiv:2001.09521 (2020)","DOI":"10.1016\/j.artmed.2021.102109"},{"issue":"10","key":"23_CR8","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":"23_CR9","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":"23_CR10","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"},{"issue":"2","key":"23_CR11","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11517-019-02050-6","volume":"58","author":"K Souadih","year":"2019","unstructured":"Souadih, K., Belaid, A., Ben Salem, D., Conze, P.-H.: Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization. Med. Biol. Eng. Comput. 58(2), 291\u2013306 (2019). https:\/\/doi.org\/10.1007\/s11517-019-02050-6","journal-title":"Med. Biol. Eng. Comput."},{"key":"23_CR12","unstructured":"Tan, M., Le Q.V.E.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of Machine Learning Research, 36th International Conference on Machine Learning (ICML), Long Beach, California, USA, vol. 97, pp. 10691\u201310700 (2019)"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Vu, M.H., Grimbergen, G., Nyholm, T., L\u00f6fstedt, T.: Evaluation of multi-slice inputs to convolutional neural networks for medical image segmentation. arXiv preprint arXiv:1912.09287 (2019)","DOI":"10.1002\/mp.14391"},{"issue":"3","key":"23_CR14","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s11263-019-01198-w","volume":"128","author":"Y Wu","year":"2019","unstructured":"Wu, Y., He, K.: Group normalization. Int. J. Comput. Vis. 128(3), 742\u2013755 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01198-w","journal-title":"Int. J. Comput. Vis."},{"issue":"2","key":"23_CR15","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.irbm.2018.01.004","volume":"39","author":"R Zaouche","year":"2018","unstructured":"Zaouche, R., et al.: Semi-automatic method for low-grade gliomas segmentation in magnetic resonance imaging. IRBM 39(2), 116\u2013128 (2018). https:\/\/doi.org\/10.1016\/j.irbm.2018.01.004","journal-title":"IRBM"}],"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_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:04:41Z","timestamp":1774487081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72084-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720834","9783030720841"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72084-1_23","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"}]}}