{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:35:20Z","timestamp":1788957320103,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031550874","type":"print"},{"value":"9783031550881","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-55088-1_10","type":"book-chapter","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T21:35:38Z","timestamp":1710192938000},"page":"109-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SoftCTM: Cell Detection by\u00a0Soft Instance Segmentation and\u00a0Consideration of\u00a0Cell-Tissue Interaction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6703-9368","authenticated-orcid":false,"given":"Lydia Anette","family":"Schoenpflug","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9206-4885","authenticated-orcid":false,"given":"Viktor Hendrik","family":"Koelzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,10]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","unstructured":"Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., Rajpoot, N.: Nuclick: a deep learning framework for interactive segmentation of microscopic images. Med. Image Anal. 65, 101771 (2020). https:\/\/doi.org\/10.1016\/j.media.2020.101771, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841520301353","DOI":"10.1016\/j.media.2020.101771"},{"key":"10_CR2","unstructured":"Bancher, B., Mahbod, A., Ellinger, I., Ecker, R., Dorffner, G.: Improving mask R-CNN for nuclei instance segmentation in hematoxylin & eosin-stained histological images. In: Atzori, M., et al. (eds.) Proceedings of the MICCAI Workshop on Computational Pathology. Proceedings of Machine Learning Research, vol. 156, pp. 20\u201335. PMLR (2021). https:\/\/proceedings.mlr.press\/v156\/bancher21a.html"},{"issue":"4","key":"10_CR3","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision - ECCV 2018","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018, pp. 833\u2013851. Springer International Publishing, Cham (2018)"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Graham, S., et al.: Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.101563, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841519301045","DOI":"10.1016\/j.media.2019.101563"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-97281-3_6","volume-title":"Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis","author":"M Jahanifar","year":"2022","unstructured":"Jahanifar, M., et al.: Stain-robust mitotic figure detection for\u00a0the\u00a0mitosis domain generalization challenge. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds.) MICCAI 2021. LNCS, vol. 13166, pp. 48\u201352. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-97281-3_6"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Liu, D., et al.: Nuclei segmentation via a deep panoptic model with semantic feature fusion. In: IJCAI, pp. 861\u2013868 (2019)","DOI":"10.24963\/ijcai.2019\/121"},{"key":"10_CR9","unstructured":"Lunit Inc.: OCELOT 2023: Cell Detection from Cell-Tissue Interaction - Grand Challenge \u2013 ocelot2023.grand-challenge.org (2023). https:\/\/ocelot2023.grand-challenge.org\/. Accessed 29 Sep 2023"},{"issue":"2","key":"10_CR10","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2019","unstructured":"Naylor, P., La\u00e9, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448\u2013459 (2019). https:\/\/doi.org\/10.1109\/TMI.2018.2865709","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Ryu, J., et al.: Ocelot: overlapped cell on tissue dataset for histopathology. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23902\u201323912 (2023)","DOI":"10.1109\/CVPR52729.2023.02289"},{"key":"10_CR12","unstructured":"Schoenpflug, L.A., Lafarge, M.W., Frei, A.L., Koelzer, V.H.: Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides. arXiv preprint arXiv:2304.03101 (2023)"},{"key":"10_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"Sun, Y., Huang, X., Zhou, H., Zhang, Q.: SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images. Med. Image Anal. 72, 102142 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.102142, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841521001882","DOI":"10.1016\/j.media.2021.102142"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"21420","DOI":"10.1109\/ACCESS.2019.2896920","volume":"7","author":"Z Zeng","year":"2019","unstructured":"Zeng, Z., Xie, W., Zhang, Y., Lu, Y.: RIC-Unet: an improved neural network based on Unet for nuclei segmentation in histology images. IEEE Access 7, 21420\u201321428 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2896920","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55088-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T03:20:35Z","timestamp":1713669635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55088-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031550874","9783031550881"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55088-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 March 2024","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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","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":"5","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)"}}]}}