{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:47:38Z","timestamp":1772603258114,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030336417","type":"print"},{"value":"9783030336424","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-33642-4_5","type":"book-chapter","created":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T06:04:25Z","timestamp":1574229865000},"page":"42-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Weakly Supervised Segmentation from Extreme Points"],"prefix":"10.1007","author":[{"given":"Holger","family":"Roth","sequence":"first","affiliation":[]},{"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Fausto","family":"Milletari","sequence":"additional","affiliation":[]},{"given":"Ziyue","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaosong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Daguang","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"issue":"3","key":"5_CR1","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1148\/radiol.2017151022","volume":"284","author":"A Devaraj","year":"2017","unstructured":"Devaraj, A., van Ginneken, B., Nair, A., Baldwin, D.: Use of volumetry for lung nodule management: theory and practice. Radiology 284(3), 630\u2013644 (2017)","journal-title":"Radiology"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5_CR3","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":"5_CR4","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision, pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"5_CR5","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"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","volume":"41","author":"G Wang","year":"2018","unstructured":"Wang, G., et al.: DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1559\u20131572 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"5_CR7","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","volume":"37","author":"G Wang","year":"2018","unstructured":"Wang, G., et al.: Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans. Med. Imaging 37(7), 1562\u20131573 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR8","unstructured":"Sakinis, T., et al.: Interactive segmentation of medical images through fully convolutional neural networks. arXiv preprint arXiv:1903.08205 (2019)"},{"key":"5_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1007\/978-3-030-00889-5_27","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"YB Can","year":"2018","unstructured":"Can, Y.B., Chaitanya, K., Mustafa, B., Koch, L.M., Konukoglu, E., Baumgartner, C.F.: Learning to segment medical images with scribble-supervision alone. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 236\u2013244. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_27"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1768\u20131783(11) (2006)","DOI":"10.1109\/TPAMI.2006.233"},{"issue":"2","key":"5_CR11","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMI.2016.2621185","volume":"36","author":"M Rajchl","year":"2017","unstructured":"Rajchl, M., et al.: DeepCut: object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans. Med. Imaging 36(2), 674\u2013683 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Cai, J., et al.: Accurate weakly supervised deep lesion segmentation on CT scans: Self-paced 3D mask generation from RECIST. arXiv preprint arXiv:1801.08614 (2018)","DOI":"10.1007\/978-3-030-00937-3_46"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gopalakrishnan, V., Lu, L., Summers, R.M., Moss, J., Yao, J.: Self-learning to detect and segment cysts in lung ct images without manual annotation. In: ISBI, pp. 1100\u20131103. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363763"},{"key":"5_CR14","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B.: Constrained-CNN losses for weakly supervised segmentation. Med. Image Anal. 54, 88\u201399 (2019)","journal-title":"Med. Image Anal."},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.media.2018.01.006","volume":"45","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., et al.: Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med. Image Anal. 45, 94\u2013107 (2018)","journal-title":"Med. Image Anal."},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L.: Deep extreme cut: From extreme points to object segmentation. In: CVPR, pp. 616\u2013625 (2018)","DOI":"10.1109\/CVPR.2018.00071"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Papadopoulos, D.P., Uijlings, J.R., Keller, F., Ferrari, V.: Extreme clicking for efficient object annotation. In: ICCV, pp. 4930\u20134939 (2017)","DOI":"10.1109\/ICCV.2017.528"},{"issue":"1","key":"5_CR18","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/BF01386390","volume":"1","author":"EW Dijkstra","year":"1959","unstructured":"Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269\u2013271 (1959)","journal-title":"Numerische mathematik"},{"key":"5_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/978-3-030-00934-2_94","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"S Liu","year":"2018","unstructured":"Liu, S., et al.: 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 851\u2013858. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_94"}],"container-title":["Lecture Notes in Computer Science","Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33642-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:02:17Z","timestamp":1732060937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33642-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336417","9783030336424"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33642-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LABELS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"labels2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccailabels.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}