{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T01:36:58Z","timestamp":1766281018588,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322441"},{"type":"electronic","value":"9783030322458"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32245-8_28","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"246-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Qi","family":"Zeng","sequence":"first","affiliation":[]},{"given":"Davood","family":"Karimi","sequence":"additional","affiliation":[]},{"given":"Emily H. T.","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Shahed","family":"Mohammed","sequence":"additional","affiliation":[]},{"given":"Caitlin","family":"Schneider","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Honarvar","sequence":"additional","affiliation":[]},{"given":"Septimiu E.","family":"Salcudean","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"28_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/978-3-030-00928-1_49","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"SMMR Al Arif","year":"2018","unstructured":"Al Arif, S.M.M.R., Knapp, K., Slabaugh, G.: SPNet: shape prediction using a fully convolutional neural network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 430\u2013439. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_49"},{"key":"28_CR2","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056 [cs.CV] (2019)"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR4","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1007\/978-3-030-00928-1_87","volume-title":"MICCAI 2018","author":"Y Hu","year":"2018","unstructured":"Hu, Y., et al.: Adversarial deformation regularization for training image registration neural networks. In: Frangi, A.F., et al. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 774\u2013782. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_87"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., ven der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE CVPR, pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"8","key":"28_CR6","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1007\/s11548-018-1785-8","volume":"13","author":"D Karimi","year":"2018","unstructured":"Karimi, D., et al.: Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models. Int. J. Comput. Assist. Radiol. Surg. 13(8), 1211\u20131219 (2018)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"28_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 [cs.LG] (2014)"},{"key":"28_CR8","unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: PMLR, vol. 38, pp. 562\u2013570 (2015)"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.: 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":"28_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-319-66182-7_19","volume-title":"MICCAI 2017","author":"F Milletari","year":"2017","unstructured":"Milletari, F., et al.: Integrating statistical prior knowledge into convolutional neural networks. In: Descoteaux, M., et al. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 161\u2013168. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_19"},{"key":"28_CR11","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML, pp. 807\u2013814 (2010)"},{"issue":"11","key":"28_CR12","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1109\/TMI.2012.2202913","volume":"31","author":"A Rasoulian","year":"2012","unstructured":"Rasoulian, A., Rohling, R., Abolmaesumi, P.: Group-wise registration of point sets for statistical shape models. IEEE Trans. Med. Imag. 31(11), 2025\u20132034 (2012)","journal-title":"IEEE Trans. Med. Imag."},{"key":"28_CR13","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"MLMI 2017","author":"SSM Salehi","year":"2017","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., et al. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379\u2013387. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_44"},{"issue":"1","key":"28_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.2214\/AJR.09.2601","volume":"193","author":"B Taouli","year":"2009","unstructured":"Taouli, B., Ehman, R.L., Reeder, S.B.: Advanced MRI methods for assessment of chronic liver disease. AJR Am. J. Roentgenol. 193(1), 14\u201327 (2009)","journal-title":"AJR Am. J. Roentgenol."},{"issue":"6","key":"28_CR15","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imag. 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imag."},{"key":"28_CR16","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-319-66179-7_58","volume-title":"MICCAI 2017","author":"D Yang","year":"2017","unstructured":"Yang, D., et al.: Automatic liver segmentation using an adversarial image-to-image network. In: Descoteaux, M., et al. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 507\u2013515. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_58"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32245-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:29:32Z","timestamp":1728520172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32245-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322441","9783030322458"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32245-8_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1730","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"539","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.07","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.31","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}