{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:38:16Z","timestamp":1774539496312,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250651","type":"print"},{"value":"9783031250668","type":"electronic"}],"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_19","type":"book-chapter","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T08:18:05Z","timestamp":1676621885000},"page":"355-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Contour Dice Loss for\u00a0Structures with\u00a0Fuzzy and\u00a0Complex Boundaries in\u00a0Fetal MRI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5668-0303","authenticated-orcid":false,"given":"Bella","family":"Specktor-Fadida","sequence":"first","affiliation":[]},{"given":"Bossmat","family":"Yehuda","sequence":"additional","affiliation":[]},{"given":"Daphna","family":"Link-Sourani","sequence":"additional","affiliation":[]},{"given":"Liat","family":"Ben-Sira","sequence":"additional","affiliation":[]},{"given":"Dafna","family":"Ben-Bashat","sequence":"additional","affiliation":[]},{"given":"Leo","family":"Joskowicz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"19_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-319-74113-0_2","volume-title":"Computational Methods and Clinical Applications in Musculoskeletal Imaging","author":"SMMR Al Arif","year":"2018","unstructured":"Al Arif, S.M.M.R., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds.) MSKI 2017. LNCS, vol. 10734, pp. 12\u201324. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-74113-0_2"},{"key":"19_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/978-3-319-46723-8_68","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"A Alansary","year":"2016","unstructured":"Alansary, A., et al.: Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 589\u2013597. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_68"},{"key":"19_CR3","unstructured":"Caliva, F., Iriondo, C., Martinez, A.M., Majumdar, S., Pedoia, V.: Distance map loss penalty term for semantic segmentation. In: International Conference on Medical Imaging with Deep Learning - Extended Abstract Track, pp. 08\u201310 (2019). https:\/\/openreview.net\/forum?id=B1eIcvS45V"},{"issue":"8","key":"19_CR4","doi-asserted-by":"publisher","first-page":"2028","DOI":"10.1093\/brain\/awn137","volume":"131","author":"J Dubois","year":"2008","unstructured":"Dubois, J., et al.: Primary cortical folding in the human newborn: an early marker of later functional development. Brain 131(8), 2028\u20132041 (2008)","journal-title":"Brain"},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1016\/j.neuroimage.2018.03.005","volume":"185","author":"J Dubois","year":"2019","unstructured":"Dubois, J., et al.: The dynamics of cortical folding waves and prematurity-related deviations revealed by spatial and spectral analysis of gyrification. Neuroimage 185, 934\u2013946 (2019)","journal-title":"Neuroimage"},{"key":"19_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-030-59725-2_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"G Dudovitch","year":"2020","unstructured":"Dudovitch, G., Link-Sourani, D., Ben Sira, L., Miller, E., Ben Bashat, D., Joskowicz, L.: Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 365\u2013374. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_35"},{"key":"19_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116324","volume":"206","author":"M Ebner","year":"2020","unstructured":"Ebner, M., et al.: An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. Neuroimage 206, 116324 (2020)","journal-title":"Neuroimage"},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"180083","DOI":"10.1109\/ACCESS.2019.2958133","volume":"7","author":"M Han","year":"2019","unstructured":"Han, M., et al.: Automatic segmentation of human placenta images with U-Net. IEEE Access 7, 180083\u2013180092 (2019)","journal-title":"IEEE Access"},{"issue":"10","key":"19_CR9","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1016\/j.mri.2010.06.024","volume":"28","author":"IA Hosny","year":"2010","unstructured":"Hosny, I.A., Elghawabi, H.S.: Ultrafast MRI of the fetus: an increasingly important tool in prenatal diagnosis of congenital anomalies. Magn. Reson. Imaging 28(10), 1431\u20131439 (2010)","journal-title":"Magn. Reson. Imaging"},{"issue":"2","key":"19_CR10","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"19_CR11","unstructured":"Jurdi, R.E., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: A surprisingly effective perimeter-based loss for medical image segmentation. In: Medical Imaging with Deep Learning, pp. 158\u2013167. PMLR (2021)"},{"issue":"2","key":"19_CR12","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi, D., Salcudean, S.E.: Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499\u2013513 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR13","unstructured":"Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 285\u2013296. PMLR (2019)"},{"issue":"3","key":"19_CR14","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s10278-021-00460-3","volume":"34","author":"KJ Kiser","year":"2021","unstructured":"Kiser, K.J., Barman, A., Stieb, S., Fuller, C.D., Giancardo, L.: Novel autosegmentation spatial similarity metrics capture the time required to correct segmentations better than traditional metrics in a thoracic cavity segmentation workflow. J. Digit. Imaging 34(3), 541\u2013553 (2021)","journal-title":"J. Digit. Imaging"},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-12939-2_8","volume-title":"Pattern Recognition","author":"O Kodym","year":"2019","unstructured":"Kodym, O., \u0160pan\u011bl, M., Herout, A.: Segmentation of head and neck organs at risk using CNN with batch dice loss. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 105\u2013114. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12939-2_8"},{"key":"19_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma, J., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)","journal-title":"Med. Image Anal."},{"key":"19_CR17","unstructured":"Nikolov, S., et al.: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. arXiv preprint arXiv:1809.04430 (2018)"},{"key":"19_CR18","unstructured":"Payette, K., et al.: Fetal brain tissue annotation and segmentation challenge results. arXiv preprint arXiv:2204.09573 (2022)"},{"key":"19_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102145","volume":"72","author":"M Pietsch","year":"2021","unstructured":"Pietsch, M., et al.: APPLAUSE: automatic prediction of placental health via U-Net segmentation and statistical evaluation. Med. Image Anal. 72, 102145 (2021)","journal-title":"Med. Image Anal."},{"key":"19_CR20","unstructured":"Quah, B., et al.: Comparison of pure deep learning approaches for placental extraction from dynamic functional MRI sequences between 19 and 37 gestational weeks. In: Proceedings of International Society for Magnetic Resonance in Medicine (2021)"},{"issue":"1","key":"19_CR21","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1097\/01.AOG.0000318871.95090.d9","volume":"112","author":"UM Reddy","year":"2008","unstructured":"Reddy, U.M., Filly, R.A., Copel, J.A.: Prenatal imaging: ultrasonography and magnetic resonance imaging. Obstet. Gynecol. 112(1), 145 (2008)","journal-title":"Obstet. Gynecol."},{"issue":"6","key":"19_CR22","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1002\/dneu.20614","volume":"68","author":"M Rutherford","year":"2008","unstructured":"Rutherford, M., et al.: MR imaging methods for assessing fetal brain development. Dev. Neurobiol. 68(6), 700\u2013711 (2008)","journal-title":"Dev. Neurobiol."},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"1884","DOI":"10.3389\/fphys.2018.01884","volume":"9","author":"N Salavati","year":"2019","unstructured":"Salavati, N., et al.: The possible role of placental morphometry in the detection of fetal growth restriction. Front. Physiol. 9, 1884 (2019)","journal-title":"Front. Physiol."},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., et al.: Real-time automatic fetal brain extraction in fetal MRI by deep learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 720\u2013724. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363675"},{"key":"19_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-030-87735-4_18","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis","author":"B Specktor-Fadida","year":"2021","unstructured":"Specktor-Fadida, B., et al.: A bootstrap self-training method for sequence transfer: state-of-the-art placenta segmentation in fetal MRI. In: Sudre, C.H., et al. (eds.) UNSURE\/PIPPI -2021. LNCS, vol. 12959, pp. 189\u2013199. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87735-4_18"},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.media.2019.03.008","volume":"54","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena, J., et al.: Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med. Image Anal. 54, 263\u2013279 (2019)","journal-title":"Med. Image Anal."},{"key":"19_CR27","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2018.10.003","volume":"51","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena, J., et al.: Segmentation and classification in MRI and us fetal imaging: recent trends and future prospects. Med. Image Anal. 51, 61\u201388 (2019)","journal-title":"Med. Image Anal."},{"key":"19_CR28","unstructured":"Yang, S., Kweon, J., Kim, Y.H.: Major vessel segmentation on X-ray coronary angiography using deep networks with a novel penalty loss function. In: International Conference on Medical Imaging with Deep Learning-Extended Abstract Track (2019)"}],"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_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:20:16Z","timestamp":1709817616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25066-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250651","9783031250668"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25066-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}