{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:49:31Z","timestamp":1743108571023,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030786083"},{"type":"electronic","value":"9783030786090"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78609-0_17","type":"book-chapter","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T23:38:40Z","timestamp":1625787520000},"page":"193-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient 3D Pancreas Segmentation Using Two-Stage 3D Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1884-3415","authenticated-orcid":false,"given":"Wenqiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aihong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuesheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"17_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/978-3-319-24553-9_68","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"HR Roth","year":"2015","unstructured":"Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556\u2013564. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24553-9_68"},{"key":"17_CR2","unstructured":"Isensee, F., Petersen, J., Kohl, S.A.A, et al.: Nnu-net: breaking the spell on successful medical image segmentation (2019)"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","volume":"66","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., Oda, H., Zhou, X., et al.: An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc. 66, 90 (2018)","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."},{"key":"17_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 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"17_CR5","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":"17_CR6","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3D residual networks. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)","DOI":"10.1109\/ICCV.2017.590"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Wang, Z.H., Liu, Z., Song, Y.Q., et al.: Densely connected deep U-Net for abdominal multi-organ segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803103"},{"key":"17_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-030-32245-8_23","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"N Zhao","year":"2019","unstructured":"Zhao, N., Tong, N., Ruan, D., Sheng, K.: Fully automated pancreas segmentation with two-stage 3D convolutional neural networks. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 201\u2013209. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_23"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826.AWERTQ (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. Int. J. Comput. Vis. (2018)","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"17_CR11","unstructured":"Lee, C., Xie, S., Gallagher, P.W., et al.: Deeply-supervised nets. Int. Conf. Artif. Intell. Stat. 562\u2013570 (2015)"},{"key":"17_CR12","unstructured":"https:\/\/pytorch.org\/"},{"key":"17_CR13","unstructured":"Duta, I.C., Liu, L., Zhu, F., et al.: Pyramidal convolution: rethinking convolutional neural networks for visual recognition. arXiv preprint arXiv:2006.11538 (2020)"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Yu, Q., Xie, L., Wang, Y., et al.: Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00864"},{"key":"17_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-319-46976-8_12","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"X Zhou","year":"2016","unstructured":"Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., Fujita, H.: Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting. In: Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J.M.R.S., Belagiannis, V., Papa, J.P., Nascimento, J.C., Loog, M., Lu, Z., Cardoso, J.S., Cornebise, J. (eds.) LABELS\/DLMIA -2016. LNCS, vol. 10008, pp. 111\u2013120. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_12"},{"issue":"2","key":"17_CR16","first-page":"63","volume":"36","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., et al.: Deep learning and its application to medical image segmentation. Med. Imaging Technol. 36(2), 63\u201371 (2018)","journal-title":"Med. Imaging Technol."},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Evan, S., Trevor, D.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"3","key":"17_CR18","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P.A., Piven, J., Hazlett, H.C., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116\u20131128 (2006)","journal-title":"Neuroimage"},{"issue":"3","key":"17_CR19","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.32604\/cmc.2020.06177","volume":"62","author":"J Cheng","year":"2020","unstructured":"Cheng, J., Liu, Y., Tang, X., Sheng, V.S., Li, M., et al.: DDOS attack detection via multi-scale convolutional neural network. Comput. Mater. Continua 62(3), 1317\u20131333 (2020)","journal-title":"Comput. Mater. Continua"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78609-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T23:53:31Z","timestamp":1625788411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78609-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030786083","9783030786090"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78609-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 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":"incodldos2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icaisconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}