{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:38:14Z","timestamp":1764175094105,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030871925"},{"type":"electronic","value":"9783030871932"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87193-2_13","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T20:25:10Z","timestamp":1632342310000},"page":"131-141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance"],"prefix":"10.1007","author":[{"given":"Hongyi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanfen","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinhao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutaro","family":"Iwamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian-Hua","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruofeng","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"unstructured":"Alsallakh, B., Kokhlikyan, N., Miglani, V., Yuan, J., Reblitz-Richardson, O.: Mind the pad - cnns can develop blind spots. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=m1CD7tPubNy","key":"13_CR1"},{"issue":"1","key":"13_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. data 4(1), 1\u201313 (2017)","journal-title":"Sci. data"},{"unstructured":"Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)","key":"13_CR3"},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-319-46723-8_48","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"PF Christ","year":"2016","unstructured":"Christ, P.F., et al.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 415\u2013423. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_48"},{"key":"13_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"},{"doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","key":"13_CR6","DOI":"10.1109\/CVPR.2019.00326"},{"doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059. IEEE (2020)","key":"13_CR7","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: Medical image segmentation with deep atlas prior. IEEE Trans. Med. Imaging (2021)","key":"13_CR8","DOI":"10.1109\/TMI.2021.3089661"},{"doi-asserted-by":"crossref","unstructured":"Huang, Y., Shao, L., Frangi, A.F.: Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6070\u20136079 (2017)","key":"13_CR9","DOI":"10.1109\/CVPR.2017.613"},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.3389\/fnins.2019.01449","volume":"13","author":"PY Kao","year":"2020","unstructured":"Kao, P.Y., et al.: Improving patch-based convolutional neural networks for MRI brain tumor segmentation by leveraging location information. Front. Neurosci. 13, 1449 (2020)","journal-title":"Front. Neurosci."},{"issue":"1","key":"13_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"H Kim","year":"2020","unstructured":"Kim, H., et al.: Abdominal multi-organ auto-segmentation using 3d-patch-based deep convolutional neural network. Sci. Rep. 10(1), 1\u20139 (2020)","journal-title":"Sci. Rep."},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)","key":"13_CR12"},{"issue":"3","key":"13_CR13","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1002\/nme.1296","volume":"63","author":"F Lekien","year":"2005","unstructured":"Lekien, F., Marsden, J.: Tricubic interpolation in three dimensions. Int. J. Numer. Methods Eng. 63(3), 455\u2013471 (2005)","journal-title":"Int. J. Numer. Methods Eng."},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-3-030-59719-1_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Z Li","year":"2020","unstructured":"Li, Z., Pan, J., Wu, H., Wen, Z., Qin, J.: Memory-efficient automatic kidney and tumor segmentation based on non-local context guided 3D U-Net. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 197\u2013206. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_20"},{"key":"13_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-030-59719-1_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"F Madesta","year":"2020","unstructured":"Madesta, F., Schmitz, R., R\u00f6sch, T., Werner, R.: Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 289\u2013298. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_29"},{"issue":"10","key":"13_CR16","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","key":"13_CR17","DOI":"10.1109\/3DV.2016.79"},{"key":"13_CR18","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":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-3-030-32226-7_34","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Q Shao","year":"2019","unstructured":"Shao, Q., Gong, L., Ma, K., Liu, H., Zheng, Y.: Attentive CT lesion detection using deep pyramid inference with multi-scale booster. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 301\u2013309. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_34"},{"key":"13_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1007\/978-3-030-59719-1_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Tang","year":"2020","unstructured":"Tang, Y., Tang, Y., Zhu, Y., Xiao, J., Summers, R.M.: E$$^2$$Net: an edge enhanced network for accurate liver and tumor segmentation on CT scans. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 512\u2013522. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_50"},{"key":"13_CR21","doi-asserted-by":"publisher","first-page":"101894","DOI":"10.1016\/j.media.2020.101894","volume":"69","author":"Y Tang","year":"2021","unstructured":"Tang, Y., et al.: High-resolution 3D abdominal segmentation with random patch network fusion. Med. Image Anal. 69, 101894 (2021)","journal-title":"Med. Image Anal."},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Li, D., Zhu, Y., Tian, L., Shan, Y.: Dual super-resolution learning for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3774\u20133783 (2020)","key":"13_CR22","DOI":"10.1109\/CVPR42600.2020.00383"},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.04.005","volume":"55","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Zhou, Y., Shen, W., Park, S., Fishman, E.K., Yuille, A.L.: Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med. Image Anal. 55, 88\u2013102 (2019)","journal-title":"Med. Image Anal."},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"101766","DOI":"10.1016\/j.media.2020.101766","volume":"65","author":"Y Xia","year":"2020","unstructured":"Xia, Y., et al.: Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Med. Image Anal. 65, 101766 (2020)","journal-title":"Med. Image Anal."},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"101842","DOI":"10.1016\/j.media.2020.101842","volume":"67","author":"H Yang","year":"2021","unstructured":"Yang, H., Shan, C., Bouwman, A., Kolen, A.F., de With, P.H.: Efficient and robust instrument segmentation in 3D ultrasound using patch-of-interest-fusenet with hybrid loss. Med. Image Anal. 67, 101842 (2021)","journal-title":"Med. Image Anal."},{"doi-asserted-by":"crossref","unstructured":"Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.A.: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","key":"13_CR26","DOI":"10.1609\/aaai.v31i1.10510"},{"key":"13_CR27","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.media.2019.07.003","volume":"57","author":"G Zeng","year":"2019","unstructured":"Zeng, G., Zheng, G.: Holistic decomposition convolution for effective semantic segmentation of medical volume images. Med. Image Anal. 57, 149\u2013164 (2019)","journal-title":"Med. Image Anal."},{"key":"13_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-32226-7_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Zlocha","year":"2019","unstructured":"Zlocha, M., Dou, Q., Glocker, B.: Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 402\u2013410. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_45"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87193-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T23:20:16Z","timestamp":1673306416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87193-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871925","9783030871932"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87193-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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","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":"4","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":"The conference was held virtually.","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)"}}]}}