{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:52:41Z","timestamp":1743069161047,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250651"},{"type":"electronic","value":"9783031250668"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25066-8_26","type":"book-chapter","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T08:18:05Z","timestamp":1676621885000},"page":"466-481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmenting Glandular Biopsy Images Using the\u00a0Separate Merged Objects Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6268-7288","authenticated-orcid":false,"given":"David","family":"Sabban","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5276-0242","authenticated-orcid":false,"given":"Ilan","family":"Shimshoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"issue":"4","key":"26_CR1","first-page":"23","volume":"3","author":"A Belsare","year":"2012","unstructured":"Belsare, A., Mushrif, M.: Histopathological image analysis using image processing techniques: an overview. Signal Image Process. 3(4), 23 (2012)","journal-title":"Signal Image Process."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2487\u20132496 (2016)","DOI":"10.1109\/CVPR.2016.273"},{"key":"26_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-319-10581-9_23","volume-title":"Machine Learning in Medical Imaging","author":"A Cohen","year":"2014","unstructured":"Cohen, A., Rivlin, E., Shimshoni, I., Sabo, E.: Colon biopsy classification using crypt architecture. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 182\u2013189. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10581-9_23"},{"key":"26_CR4","unstructured":"Demir, C., Yener, B.: Automated cancer diagnosis based on histopathological images: a systematic survey. Rensselaer Polytechnic Institute, Technical report (2005)"},{"key":"26_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147\u2013171 (2009)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"26_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-58467-9","volume":"10","author":"O Iizuka","year":"2020","unstructured":"Iizuka, O., Kanavati, F., Kato, K., Rambeau, M., Arihiro, K., Tsuneki, M.: Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci. Rep. 10(1), 1\u201311 (2020)","journal-title":"Sci. Rep."},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Jenkins, D., et al.: Guidelines for the initial biopsy diagnosis of suspected chronic idiopathic inflammatory bowel disease. The British society of gastroenterology initiative. J. Clin. Pathol. 50(2), 93 (1997)","DOI":"10.1136\/jcp.50.2.93"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"4","key":"26_CR9","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","volume":"7","author":"JA Nelder","year":"1965","unstructured":"Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308\u2013313 (1965)","journal-title":"Comput. J."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Pradhan, P., et al.: Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: a SegNet-based application. In: ICPRAM, pp. 396\u2013405 (2019)","DOI":"10.5220\/0007314003960405"},{"key":"26_CR11","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":"26_CR12","unstructured":"Rubin, R., Strayer, D.S., Rubin, E., et al.: Rubin\u2019s Pathology: Clinicopathologic Foundations of Medicine. Lippincott Williams & Wilkins (2008)"},{"key":"26_CR13","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"key":"26_CR14","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-981-13-8950-4_25","volume-title":"Neural Approaches to Dynamics of Signal Exchanges","author":"L Rundo","year":"2020","unstructured":"Rundo, L., et al.: CNN-based prostate zonal segmentation on T2-weighted MR images: a\u00a0cross-dataset study. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) Neural Approaches to Dynamics of Signal Exchanges. SIST, vol. 151, pp. 269\u2013280. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-13-8950-4_25"},{"key":"26_CR15","unstructured":"Shahin, S.A.: Colon Biopsy Classification by Improving the Pixel Level Classification Stage. University of Haifa (Israel) (2018)"},{"key":"26_CR16","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the GlaS challenge contest. Med. Image Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Image Anal."},{"key":"26_CR17","unstructured":"Smochin\u0103, C., Herghelegiu, P., Manta, V.: Image processing techniques used in microscopic image segmentation. In: Technical Report. Gheorghe Asachi Technical University of Ia\u015fi (2011)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"26_CR19","unstructured":"Talo, M.: Convolutional neural networks for multi-class histopathology image classification. arXiv preprint arXiv:1903.10035 (2019)"},{"issue":"10","key":"26_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranon.2021.101174","volume":"14","author":"Y Xi","year":"2021","unstructured":"Xi, Y., Xu, P.: Global colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 14(10), 101174 (2021)","journal-title":"Transl. Oncol."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25066-8_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:22:28Z","timestamp":1709817748000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25066-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250651","9783031250668"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25066-8_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}