{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:50:29Z","timestamp":1771037429258,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030983840","type":"print"},{"value":"9783030983857","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-98385-7_14","type":"book-chapter","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T04:44:03Z","timestamp":1648183443000},"page":"103-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Ensemble of\u00a03D U-Net Based Models for\u00a0Segmentation of\u00a0Kidney and\u00a0Masses in\u00a0CT Scans"],"prefix":"10.1007","author":[{"given":"Alex","family":"Golts","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Khapun","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Shats","sequence":"additional","affiliation":[]},{"given":"Yoel","family":"Shoshan","sequence":"additional","affiliation":[]},{"given":"Flora","family":"Gilboa-Solomon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"14_CR1","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056 (2019)"},{"issue":"11","key":"14_CR2","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","volume":"23","author":"Y Boykov","year":"2001","unstructured":"Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222\u20131239 (2001)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR3","unstructured":"Chen, Z.Z.: A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge (2021). https:\/\/openreview.net\/forum?id=6Py5BNBKoJt"},{"key":"14_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"issue":"5","key":"14_CR5","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.eururo.2009.07.040","volume":"56","author":"V Ficarra","year":"2009","unstructured":"Ficarra, V., et al.: Preoperative aspects and dimensions used for an anatomical (PADUA) classification of renal tumours in patients who are candidates for nephron-sparing surgery. Eur. Urol. 56(5), 786\u2013793 (2009)","journal-title":"Eur. Urol."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"George, Y.M.: A coarse-to-fine 3D U-Net network for semantic segmentation of kidney CT scans (2021). https:\/\/openreview.net\/forum?id=dvZiPuZk-Bc","DOI":"10.1007\/978-3-030-98385-7_18"},{"issue":"2","key":"14_CR7","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/JSTSP.2021.3049634","volume":"15","author":"A Golts","year":"2021","unstructured":"Golts, A., Freedman, D., Elad, M.: Deep energy: task driven training of deep neural networks. IEEE J. Sel. Top. Sig. Process. 15(2), 324\u2013338 (2021)","journal-title":"IEEE J. Sel. Top. Sig. Process."},{"key":"14_CR8","doi-asserted-by":"crossref","first-page":"101821","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the KiTS19 challenge. Med. Image Anal. 67, 101821 (2021)","journal-title":"Med. Image Anal."},{"key":"14_CR9","unstructured":"Heller, N., et al.: The KiTS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes. arXiv preprint arXiv:1904.00445 (2019)"},{"key":"14_CR10","doi-asserted-by":"publisher","unstructured":"IBM Research, Haifa: FuseMedML (2021). https:\/\/doi.org\/10.5281\/ZENODO.5146491. https:\/\/zenodo.org\/record\/5146491. https:\/\/github.com\/IBM\/fuse-med-ml","DOI":"10.5281\/ZENODO.5146491"},{"key":"14_CR11","unstructured":"Isensee, F.: nnU-Net baseline for the KiTS21 task (2021). https:\/\/github.com\/neheller\/kits21\/tree\/master\/examples\/nnUNet_baseline"},{"issue":"2","key":"14_CR12","doi-asserted-by":"crossref","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"},{"issue":"3","key":"14_CR13","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.juro.2009.05.035","volume":"182","author":"A Kutikov","year":"2009","unstructured":"Kutikov, A., Uzzo, R.G.: The RENAL nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth. J. Urol. 182(3), 844\u2013853 (2009)","journal-title":"J. Urol."},{"key":"14_CR14","unstructured":"National Cancer Institute: Common cancer types (2021). https:\/\/www.cancer.gov\/types\/common-cancers"},{"key":"14_CR15","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":"14_CR16","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":"14_CR17","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1016\/j.juro.2010.01.005","volume":"5","author":"MN Simmons","year":"2010","unstructured":"Simmons, M.N., Ching, C.B., Samplaski, M.K., Park, C.H., Gill, I.S.: Kidney tumor location measurement using the C index method. J. Urol. 5, 1708\u20131713 (2010)","journal-title":"J. Urol."},{"key":"14_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/978-3-030-00937-3_53","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"A Taha","year":"2018","unstructured":"Taha, A., Lo, P., Li, J., Zhao, T.: Kid-Net: convolution networks for kidney vessels segmentation from CT-volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 463\u2013471. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_53"},{"key":"14_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/978-3-030-01270-0_31","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Tang","year":"2018","unstructured":"Tang, M., Perazzi, F., Djelouah, A., Ayed, I.B., Schroers, C., Boykov, Y.: On regularized losses for weakly-supervised CNN segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 524\u2013540. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_31"},{"key":"14_CR20","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)"},{"key":"14_CR21","unstructured":"Wikipedia: list of cancer mortality rates in the united states (2021). https:\/\/en.wikipedia.org\/wiki\/List_of_cancer_mortality_rates_in_the_United_States"},{"key":"14_CR22","unstructured":"Wu, M., Liu, Z.: Less is more (2021). https:\/\/openreview.net\/forum?id=immB02xhM15"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Yang, X., Jianpeng, Z., Yong, X.: Transfer learning for KiTS21 challenge (2021). https:\/\/openreview.net\/forum?id=XXtHQy0d8Y","DOI":"10.1007\/978-3-030-98385-7_21"}],"container-title":["Lecture Notes in Computer Science","Kidney and Kidney Tumor Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98385-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T16:05:11Z","timestamp":1648224311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98385-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030983840","9783030983857"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98385-7_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KiTS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Challenge on Kidney and Kidney Tumor Segmentation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"kits2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/kits21.kits-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Openreview.net","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","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":"21","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":"72% - 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":"3","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":"The challenge was held online due to the COVID-19 pandemic","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)"}}]}}