{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:43:23Z","timestamp":1742913803609,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031210136"},{"type":"electronic","value":"9783031210143"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21014-3_31","type":"book-chapter","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:43:40Z","timestamp":1671111820000},"page":"298-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CircleSnake: Instance Segmentation with\u00a0Circle Representation"],"prefix":"10.1007","author":[{"given":"Ethan H.","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haichun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuhayr","family":"Asad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruining","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agnes B.","family":"Fogo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuankai","family":"Huo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105273","volume":"184","author":"G Bueno","year":"2020","unstructured":"Bueno, G., Fernandez-Carrobles, M.M., Gonzalez-Lopez, L., Deniz, O.: Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput. Methods Programs Biomed. 184, 105273 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"6","key":"31_CR2","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1053\/j.ajkd.2012.12.019","volume":"61","author":"VD D\u2019Agati","year":"2013","unstructured":"D\u2019Agati, V.D., Mengel, M.: The rise of renal pathology in nephrology: structure illuminates function. Am. J. Kidney Dis. 61(6), 1016\u20131025 (2013)","journal-title":"Am. J. Kidney Dis."},{"key":"31_CR3","unstructured":"Gadermayr, M., Dombrowski, A.K., Klinkhammer, B.M., Boor, P., Merhof, D.: CNN cascades for segmenting whole slide images of the kidney. arXiv preprint arXiv:1708.00251 (2017)"},{"issue":"10","key":"31_CR4","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1681\/ASN.2018121259","volume":"30","author":"B Ginley","year":"2019","unstructured":"Ginley, B., Lutnick, B., Jen, K.Y., Fogo, A.B., Jain, S., Rosenberg, A., Walavalkar, V., Wilding, G., Tomaszewski, J.E., Yacoub, R., et al.: Computational segmentation and classification of diabetic glomerulosclerosis. J. Am. Soc. Nephrol. 30(10), 1953\u20131967 (2019)","journal-title":"J. Am. Soc. Nephrol."},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Heckenauer, R., et al.: Real-time detection of glomeruli in renal pathology. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 350\u2013355. IEEE (2020)","DOI":"10.1109\/CBMS49503.2020.00072"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Huo, Y., Deng, R., Liu, Q., Fogo, A.B., Yang, H.: Ai applications in renal pathology. Kidney International (2021)","DOI":"10.1016\/j.kint.2021.01.015"},{"issue":"7","key":"31_CR8","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1016\/j.ekir.2019.04.008","volume":"4","author":"S Kannan","year":"2019","unstructured":"Kannan, S., Morgan, L.A., Liang, B., Cheung, M.G., Lin, C.Q., Mun, D., Nader, R.G., Belghasem, M.E., Henderson, J.M., Francis, J.M., et al.: Segmentation of glomeruli within trichrome images using deep learning. Kidney international reports 4(7), 955\u2013962 (2019)","journal-title":"Kidney international reports"},{"issue":"7","key":"31_CR9","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3390\/jimaging4070091","volume":"4","author":"Y Kawazoe","year":"2018","unstructured":"Kawazoe, Y.: Faster R-CNN-based glomerular detection in multistained human whole slide images. J. Imaging 4(7), 91 (2018)","journal-title":"J. Imaging"},{"issue":"3","key":"31_CR10","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","volume":"128","author":"Hei Law","year":"2019","unstructured":"Law, Hei, Deng, Jia: CornerNet: Detecting Objects as Paired Keypoints. Int. J. Comput. Vis. 128(3), 642\u2013656 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01204-1","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"31_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: Common Objects in Context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"31_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/978-3-030-04239-4_33","volume-title":"Neural Information Processing","author":"Y-C Lo","year":"2018","unstructured":"Lo, Y.-C.: Glomerulus Detection on Light Microscopic Images of Renal Pathology with the Faster R-CNN. In: Cheng, Long, Leung, Andrew Chi Sing., Ozawa, Seiichi (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 369\u2013377. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04239-4_33"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Luo, X., et al.: SCPM-net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching. arXiv preprint arXiv:2104.05215 (2021)","DOI":"10.1016\/j.media.2021.102287"},{"key":"31_CR15","unstructured":"Nguyen, E.H., et al.: Circle representation for medical object detection. IEEE Transactions on Medical Imaging (2021)"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X.: Deep snake for real-time instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8533\u20138542 (2020)","DOI":"10.1109\/CVPR42600.2020.00856"},{"issue":"1","key":"31_CR17","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1097\/MNH.0b013e3283410a7d","volume":"20","author":"VG Puelles","year":"2011","unstructured":"Puelles, V.G., Hoy, W.E., Hughson, M.D., Diouf, B., Douglas-Denton, R.N., Bertram, J.F.: Glomerular number and size variability and risk for kidney disease. Curr. Opin. Nephrol. Hypertens. 20(1), 7\u201315 (2011)","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"31_CR18","unstructured":"Rehem, J.M.C., Dos Santos, W.L.C., Duarte, A.A., De Oliveira, L.R., Angelo, M.F.: Automatic glomerulus detection in renal histological images. In: Medical Imaging 2021: Digital Pathology, vol. 11603, p. 116030K. International Society for Optics and Photonics (2021)"},{"key":"31_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-59719-1_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"H Yang","year":"2020","unstructured":"Yang, H., et al.: Circle Net: Anchor-Free Glomerulus Detection with Circle Representation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 35\u201344. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_4"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403\u20132412 (2018)","DOI":"10.1109\/CVPR.2018.00255"},{"key":"31_CR21","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21014-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:49:19Z","timestamp":1671112159000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21014-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031210136","9783031210143"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21014-3_31","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":"16 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2022\/","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":"64","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":"48","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":"75% - 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)"}}]}}