{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:26:08Z","timestamp":1743017168480,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030326913"},{"type":"electronic","value":"9783030326920"}],"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\/978-3-030-32692-0_4","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T12:04:21Z","timestamp":1570622661000},"page":"27-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7251-9464","authenticated-orcid":false,"given":"Hykoush","family":"Asaturyan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. Louise","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julie","family":"Fitzpatrick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimmy D.","family":"Bell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Villarini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"5","key":"4_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0126825","volume":"10","author":"M Macauley","year":"2015","unstructured":"Macauley, M., Percival, K., Thelwall, P.E., Hollingsworth, K.G., Taylor, R.: Altered volume, morphology and composition of the pancreas in type 2 diabetes. PLoS ONE 10(5), 1\u201314 (2015)","journal-title":"PLoS ONE"},{"issue":"6","key":"4_CR2","first-page":"310","volume":"35","author":"A Omeri","year":"2017","unstructured":"Omeri, A., et al.: Contour variations of the body and tail of the pancreas: evaluation with MDCT. J. Radiol. 35(6), 310\u2013318 (2017)","journal-title":"J. Radiol."},{"key":"4_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/978-3-319-46723-8_51","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"J Cai","year":"2016","unstructured":"Cai, J., Lu, L., Zhang, Z., Xing, F., Yang, L., Yin, Q.: Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 442\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_51"},{"key":"4_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-642-28557-8_22","volume-title":"Abdominal Imaging. Computational and Clinical Applications","author":"T Okada","year":"2012","unstructured":"Okada, T., et al.: Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) ABD-MICCAI 2011. LNCS, vol. 7029, pp. 173\u2013180. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28557-8_22"},{"issue":"1","key":"4_CR5","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s11548-009-0384-0","volume":"5","author":"A Shimizu","year":"2010","unstructured":"Shimizu, A., Kimoto, T., Kobatake, H., Nawano, S., Shinozaki, K.: Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int. J. Comput. Assist. Radiol. Surg. 5(1), 85\u201398 (2010)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2015.06.009","volume":"26","author":"T Okada","year":"2015","unstructured":"Okada, T., Linguraru, M.G., Hori, M., Summers, R., Tomiyama, N., Sato, Y.: Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med. Image Anal. 26, 1\u201318 (2015)","journal-title":"Med. Image Anal."},{"key":"4_CR7","unstructured":"Cai, J., Lu, L., Xie, Y., Xing, F., Yang, L.: Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. CoRR, abs\/1707.04912 (2017)"},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TIP.2016.2624198","volume":"26","author":"A Farag","year":"2017","unstructured":"Farag, A., Lu, L., Roth, H.R., Liu, J., Turkbey, E., Summers, R.M.: A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Trans. Image 26(1), 386\u2013399 (2017)","journal-title":"IEEE Trans. Image"},{"key":"4_CR9","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv:1511.00561 (2015)"},{"key":"4_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"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","volume":"37","author":"E Gibson","year":"2018","unstructured":"Gibson, E., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-Networks. IEEE Trans. Med. Imaging 37, 1822\u20131834 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.04.004","volume":"75","author":"H Asaturyan","year":"2019","unstructured":"Asaturyan, H., Gligorievski, A., Villarini, B.: Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation. Comput. Med. Imaging Graph. 75, 1\u201313 (2019)","journal-title":"Comput. Med. Imaging Graph."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE ICCV, pp. 1395\u20131403 (2015)","DOI":"10.1109\/ICCV.2015.164"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650\u20132658 (2015)","DOI":"10.1109\/ICCV.2015.304"},{"key":"4_CR15","unstructured":"Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621 (2017)"},{"key":"4_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-319-93000-8_64","volume-title":"Image Analysis and Recognition","author":"H Asaturyan","year":"2018","unstructured":"Asaturyan, H., Villarini, B.: Hierarchical framework for automatic pancreas segmentation in MRI using continuous max-flow and min-cuts approach. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 562\u2013570. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93000-8_64"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Yuan, J., Bae, E., Tai, X.-C.: A study on continuous max-flow and min-cut approaches. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2217\u20132224 (2010)","DOI":"10.1109\/CVPR.2010.5539903"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth Fourth International Conference on 3D Vision, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"4_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-50835-1_22","volume-title":"Advances in Visual Computing","author":"MA Rahman","year":"2016","unstructured":"Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 234\u2013244. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50835-1_22"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32692-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:02:01Z","timestamp":1728432121000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32692-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030326913","9783030326920"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32692-0_4","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":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mlmi2019.web.unc.edu\/","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":"158","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":"78","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":"49% - 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":"2","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":"4","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":"No","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"}]}}