{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T15:10:09Z","timestamp":1767453009393,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030983840"},{"type":"electronic","value":"9783030983857"}],"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_9","type":"book-chapter","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T04:44:03Z","timestamp":1648183443000},"page":"59-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Kidney and Kidney Tumor Segmentation Using a Two-Stage Cascade Framework"],"prefix":"10.1007","author":[{"given":"Chaonan","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongda","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"9_CR1","unstructured":"Choudhari, K., Sharma, R., Halarnkar, P.: Kidney and tumor segmentation using U-Net deep learning model. In: 5th International Conference on Next Generation Computing Technologies (NGCT 2019) (2020)"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Yang, G., Li, G., Pan, T., Kong, Y., Zhu, X.: Automatic segmentation of kidney and renal tumor in CT images based on 3D fully convolutional neural network with pyramid pooling module. In: 2018 24th International Conference on Pattern Recognition (ICPR) (2018)","DOI":"10.1109\/ICPR.2018.8545143"},{"key":"9_CR3","doi-asserted-by":"crossref","first-page":"4060","DOI":"10.1109\/TIP.2019.2905537","volume":"28","author":"Q Yu","year":"2019","unstructured":"Yu, Q., Shi, Y., Sun, J., Gao, Y., Zhu, J., Dai, Y.: Crossbar-Net: a novel convolutional neural network for kidney tumor segmentation in CT images. IEEE Trans. Image Process. 28, 4060\u20134074 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Isensee, F., Maier-Hein, K.H.: An attempt at beating the 3D U-Net (2019)","DOI":"10.24926\/548719.001"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Guo, J., Zeng, W., Yu, S., Xiao, J.: RAU-Net: U-Net model based on residual and attention for kidney and kidney tumor segmentation. In: 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 353\u2013356. IEEE (2021)","DOI":"10.1109\/ICCECE51280.2021.9342530"},{"key":"9_CR6","doi-asserted-by":"crossref","first-page":"100357","DOI":"10.1016\/j.imu.2020.100357","volume":"19","author":"W Zhao","year":"2020","unstructured":"Zhao, W., Jiang, D., Queralta, J.P., Westerlund, T.: MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net. Inform. Med. Unlocked 19, 100357 (2020)","journal-title":"Inform. Med. Unlocked"},{"key":"9_CR7","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/978-981-15-8697-2_57","volume-title":"Computer Vision, Pattern Recognition, Image Processing, and Graphics","author":"D Sabarinathan","year":"2020","unstructured":"Sabarinathan, D., Parisa Beham, M., Mansoor Roomi, S.M.M.: Hyper vision net: kidney tumor segmentation using coordinate convolutional layer and attention unit. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds.) NCVPRIPG 2019. CCIS, vol. 1249, pp. 609\u2013618. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-8697-2_57"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Cheng, J., Liu, J., et al.: A double cascaded framework based on 3D SEAU-Net for kidney and kidney tumor segmentation (2019)","DOI":"10.24926\/548719.067"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Hou, X., Xie, C., Li, F., Wang, J., Nan, Y.: A triple-stage self-guided network for kidney tumor segmentation. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (2020)","DOI":"10.1109\/ISBI45749.2020.9098609"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Causey, J., et al.: An ensemble of u-net models for kidney tumor segmentation with CT images. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2021)","DOI":"10.1109\/TCBB.2021.3085608"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, Y., Hou, F., et al.: Cascaded volumetric convolutional network for kidney tumor segmentation from CT volumes (2019)","DOI":"10.24926\/548719.004"},{"issue":"2","key":"9_CR12","doi-asserted-by":"crossref","first-page":"5738","DOI":"10.1002\/cpe.5738","volume":"32","author":"X Xie","year":"2020","unstructured":"Xie, X., Li, L., Lian, S., Chen, S., Luo, Z.: SERU: a cascaded SE-ResNeXT U-Net for kidney and tumor segmentation. Concurr. Comput. Pract. Exp. 32(2), 5738 (2020)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Yan, X., Yuan, K., Zhao, W., Wang, S., Cui, S.: An efficient hybrid model for kidney tumor segmentation in CT images. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (2020)","DOI":"10.1109\/ISBI45749.2020.9098325"},{"key":"9_CR14","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":"9_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-319-75238-9_16","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"G Wang","year":"2018","unstructured":"Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178\u2013190. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_16"},{"key":"9_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"}],"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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T06:53:13Z","timestamp":1700376793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98385-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030983840","9783030983857"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98385-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}