{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:24Z","timestamp":1759358484605,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597214"},{"type":"electronic","value":"9783030597221"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59722-1_74","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T17:03:01Z","timestamp":1601658181000},"page":"765-774","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Retinal Image Segmentation with a Structure-Texture Demixing Network"],"prefix":"10.1007","author":[{"given":"Shihao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanwu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanxia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingkui","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"74_CR1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420037005","volume-title":"Automated Image Detection of Retinal Pathology","author":"H Jelinek","year":"2009","unstructured":"Jelinek, H., Cree, M.J.: Automated Image Detection of Retinal Pathology. Crc Press, Boca Raton (2009)"},{"issue":"2","key":"74_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/S0953-4431(99)00012-0","volume":"11","author":"O Hancox","year":"1999","unstructured":"Hancox, O., Michael, D.: Optic disc size, an important consideration in the glaucoma evaluation. Clin. Eye Vis. Care 11(2), 59\u201362 (1999)","journal-title":"Clin. Eye Vis. Care"},{"key":"74_CR3","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"10","key":"74_CR4","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., Cheng, J., et al.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"74_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/978-3-030-32239-7_49","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Fu, H., Dai, H., Shen, J., Pang, Y., Shao, L.: Et-net: a generic edge-attention guidance network for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 442\u2013450. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_49"},{"key":"74_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1007\/978-3-030-32239-7_40","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., et al.: From whole slide imaging to microscopy: deep microscopy adaptation network for histopathology cancer image classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 360\u2013368. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_40"},{"key":"74_CR7","doi-asserted-by":"publisher","first-page":"7834","DOI":"10.1109\/TIP.2020.3006377","volume":"29","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Wei, Y., et al.: Collaborative unsupervised domain adaptation for medical image diagnosis. IEEE Trans. Image Process. 29, 7834\u20137844 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"74_CR8","unstructured":"Geirhos, R., Rubisch, P., et al.: Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. International Conference on Learning Representations (2019)"},{"issue":"2","key":"74_CR9","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2016","unstructured":"Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982\u2013993 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"74_CR10","doi-asserted-by":"crossref","unstructured":"Revaud, J., Weinzaepfel, P., et al.: Epicflow: edge-preserving interpolation of correspondences for optical flow. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298720"},{"key":"74_CR11","doi-asserted-by":"crossref","unstructured":"Gastal, E.S., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM SIGGRAPH 2011 papers (2011)","DOI":"10.1145\/1964921.1964964"},{"issue":"6","key":"74_CR12","first-page":"1","volume":"31","author":"L Xu","year":"2012","unstructured":"Xu, L., Yan, Q., et al.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 1\u201310 (2012)","journal-title":"ACM Trans. Graph."},{"issue":"6","key":"74_CR13","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1109\/TIP.2018.2889531","volume":"28","author":"Y Kim","year":"2018","unstructured":"Kim, Y., Ham, B., Do, M.N., Sohn, K.: Structure-texture image decomposition using deep variational priors. IEEE Trans. Image Process. 28(6), 2692\u20132704 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"74_CR14","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu, H., Cheng, J., et al.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597\u20131605 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1\u20134","key":"74_CR15","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1\u20134), 259\u2013268 (1992)","journal-title":"Physica D"},{"issue":"1","key":"74_CR16","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s11263-006-4331-z","volume":"67","author":"J-F Aujol","year":"2006","unstructured":"Aujol, J.-F., Gilboa, G., et al.: Structure-texture image decomposition modeling, algorithms, and parameter selection. Int. J. Comput. Vis. 67(1), 111\u2013136 (2006). https:\/\/doi.org\/10.1007\/s11263-006-4331-z","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"74_CR17","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1109\/78.564179","volume":"45","author":"S Alliney","year":"1997","unstructured":"Alliney, S.: A property of the minimum vectors of a regularizing functional defined by means of the absolute norm. IEEE Trans. Signal Process. 45(4), 913\u2013917 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"74_CR18","doi-asserted-by":"crossref","unstructured":"Ogasawara, E., Martinez, L.C., De Oliveira, D., Zimbr\u00e3o, G., Pappa, G.L., Mattoso, M.: Adaptive normalization: a novel data normalization approach for non-stationary time series. In: The 2010 International Joint Conference on Neural Networks. IEEE (2010)","DOI":"10.1109\/IJCNN.2010.5596746"},{"issue":"4","key":"74_CR19","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"74_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/978-3-030-32239-7_88","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Zhang","year":"2019","unstructured":"Zhang, S., et al.: Attention guided network for retinal image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 797\u2013805. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_88"},{"issue":"1","key":"74_CR21","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","volume":"35","author":"Q Li","year":"2016","unstructured":"Li, Q., Feng, B., et al.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109\u2013118 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"74_CR22","doi-asserted-by":"crossref","unstructured":"Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Transactions on Medical Imaging (2016)","DOI":"10.1109\/TMI.2016.2546227"},{"key":"74_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-00934-2_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Wu","year":"2018","unstructured":"Wu, Y., Xia, Y., Song, Y., Zhang, Y., Cai, W.: Multiscale network followed network model for retinal vessel segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 119\u2013126. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_14"},{"key":"74_CR24","unstructured":"Yin, F., Liu, J., et al.: Model-based optic nerve head segmentation on retinal fundus images. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2011)"},{"issue":"6","key":"74_CR25","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TMI.2013.2247770","volume":"32","author":"J Cheng","year":"2013","unstructured":"Cheng, J., Liu, J., et al.: Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging 32(6), 1019\u20131032 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"74_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1007\/978-3-319-10404-1_98","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"Y Xn","year":"2014","unstructured":"Xn, Y., et al.: Optic cup segmentation for glaucoma detection using low-rank superpixel representation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 788\u2013795. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10404-1_98"},{"key":"74_CR27","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing. IEEE (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"74_CR28","doi-asserted-by":"publisher","first-page":"101570","DOI":"10.1016\/j.media.2019.101570","volume":"59","author":"JI Orlando","year":"2020","unstructured":"Orlando, J.I., Fu, H., et al.: REFUGE challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59722-1_74","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:09:10Z","timestamp":1759356550000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59722-1_74"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597214","9783030597221"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59722-1_74","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 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)"}}]}}