{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:12:11Z","timestamp":1743001931577,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031168758"},{"type":"electronic","value":"9783031168765"}],"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\/978-3-031-16876-5_13","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:03:00Z","timestamp":1663196580000},"page":"126-136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Facing Annotation Redundancy: OCT Layer Segmentation with\u00a0only\u00a010 Annotated Pixels per\u00a0Layer"],"prefix":"10.1007","author":[{"given":"Yanyu","family":"Xu","sequence":"first","affiliation":[]},{"given":"Xinxing","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Rick Siow Mong","family":"Goh","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Antony, B.J., et al.: A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes. Biomed. Opt. Express 4(12), 2712\u20132728 (2013)","DOI":"10.1364\/BOE.4.002712"},{"issue":"1","key":"13_CR2","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1167\/iovs.15-17281","volume":"57","author":"JC Bavinger","year":"2016","unstructured":"Bavinger, J.C., et al.: The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry. Invest. Ophthalmol. Vis. Sci. 57(1), 208\u2013217 (2016)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"4","key":"13_CR3","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1364\/BOE.5.001062","volume":"5","author":"A Carass","year":"2014","unstructured":"Carass, A., Lang, A., Hauser, M., Calabresi, P.A., Ying, H.S., Prince, J.L.: Multiple-object geometric deformable model for segmentation of macular OCT. Biomed. Opt. Express 5(4), 1062\u20131074 (2014)","journal-title":"Biomed. Opt. Express"},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/978-3-030-59710-8_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"K Fang","year":"2020","unstructured":"Fang, K., Li, W.-J.: DMNet: difference minimization network for semi-supervised segmentation in medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 532\u2013541. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_52"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Farsiu, S., et al.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121(1), 162\u2013172 (2014)","DOI":"10.1016\/j.ophtha.2013.07.013"},{"issue":"9","key":"13_CR6","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1109\/TMI.2009.2016958","volume":"28","author":"MK Garvin","year":"2009","unstructured":"Garvin, M.K., Abramoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imaging 28(9), 1436\u20131447 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR7","unstructured":"He, Y., et al.: Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks. arXiv preprint arXiv:1803.05120 (2018)"},{"key":"13_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-32239-7_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y He","year":"2019","unstructured":"He, Y., et al.: Fully convolutional boundary regression for retina OCT segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 120\u2013128. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_14"},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.dib.2018.12.073","volume":"22","author":"Y He","year":"2019","unstructured":"He, Y., Carass, A., Solomon, S.D., Saidha, S., Calabresi, P.A., Prince, J.L.: Retinal layer parcellation of optical coherence tomography images: data resource for multiple sclerosis and healthy controls. Data Brief 22, 601\u2013604 (2019)","journal-title":"Data Brief"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-030-59710-8_43","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y He","year":"2020","unstructured":"He, Y., Carass, A., Zuo, L., Dewey, B.E., Prince, J.L.: Self domain adapted network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 437\u2013446. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_43"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Kansal, V., Armstrong, J.J., Pintwala, R., Hutnik, C.: Optical coherence tomography for glaucoma diagnosis: an evidence based meta-analysis. PLoS ONE 13(1), e0190621 (2018)","DOI":"10.1371\/journal.pone.0190621"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Keane, P.A., et al.: Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 50(7), 3378\u20133385 (2009)","DOI":"10.1167\/iovs.08-2728"},{"key":"13_CR13","doi-asserted-by":"publisher","unstructured":"Khan, S., Shahin, A.H., Villafruela, J., Shen, J., Shao, L.: Extreme points derived confidence map as a cue for class-agnostic interactive segmentation using deep neural network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 66\u201373. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_8","DOI":"10.1007\/978-3-030-32245-8_8"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Lang, A., et al.: Retinal layer segmentation of macular oct images using boundary classification. Biomed. Opt. Express 4(7), 1133\u20131152 (2013)","DOI":"10.1364\/BOE.4.001133"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Li, D., Dharmawan, D.A., Ng, B.P., Rahardja, S.: Residual U-Net for retinal vessel segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1425\u20131429. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803101"},{"key":"13_CR16","doi-asserted-by":"publisher","unstructured":"Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_54","DOI":"10.1007\/978-3-030-59710-8_54"},{"key":"13_CR17","doi-asserted-by":"publisher","unstructured":"Liu, H., et al.: Simultaneous alignment and surface regression using hybrid 2D-3D networks for 3D coherent layer segmentation of retina OCT images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 108\u2013118. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_11","DOI":"10.1007\/978-3-030-87237-3_11"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Novosel, J., Vermeer, K.A., De Jong, J.H., Wang, Z., Van Vliet, L.J.: Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas. IEEE Trans. Med. Imaging 36(6), 1276\u20131286 (2017)","DOI":"10.1109\/TMI.2017.2666045"},{"key":"13_CR19","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., dAlch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Roy, A.G., et al.: ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627\u20133642 (2017)","DOI":"10.1364\/BOE.8.003627"},{"key":"13_CR21","unstructured":"Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: NeurIPS (2017)"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Wang, G., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., Zhang, S.: Uncertainty-guided efficient interactive refinement of fetal brain segmentation from stacks of MRI slices. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 279\u2013288. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_28","DOI":"10.1007\/978-3-030-59719-1_28"},{"key":"13_CR23","doi-asserted-by":"publisher","unstructured":"Wang, Y., et al.: Double-uncertainty weighted method for semi-supervised learning. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 542\u2013551. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_53","DOI":"10.1007\/978-3-030-59710-8_53"},{"key":"13_CR24","unstructured":"Xie, H., et al.: Globally optimal segmentation of mutually interacting surfaces using deep learning. arXiv preprint arXiv:2007.01259 (2020)"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"Xu, Y., et al.: Partially-supervised learning for vessel segmentation in ocular images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 271\u2013281. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_26","DOI":"10.1007\/978-3-030-87193-2_26"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: Crowd counting with partial annotations in an image. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15570\u201315579 (2021)","DOI":"10.1109\/ICCV48922.2021.01528"},{"key":"13_CR27","doi-asserted-by":"publisher","unstructured":"Yazdanpanah, A., Hamarneh, G., Smith, B., Sarunic, M.: Intra-retinal layer segmentation in optical coherence tomography using an active contour approach. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 649\u2013656. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04271-3_79","DOI":"10.1007\/978-3-642-04271-3_79"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Chen, H., Lin, H., Heng, P.-A.: Deep semi-supervised knowledge distillation for overlapping cervical cell instance segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 521\u2013531. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_51","DOI":"10.1007\/978-3-030-59710-8_51"}],"container-title":["Lecture Notes in Computer Science","Resource-Efficient Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16876-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:19:13Z","timestamp":1663197553000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16876-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031168758","9783031168765"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16876-5_13","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":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"REMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Resource-Efficient Medical Image Analysis","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":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"remia2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-remia.github.io\/","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":"19","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":"13","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":"68% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}