{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:17:04Z","timestamp":1778779024254,"version":"3.51.4"},"reference-count":43,"publisher":"IOP Publishing","issue":"24","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62176190"],"award-info":[{"award-number":["62176190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2023,12,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Choroidal vessels account for 85% of all blood vessels in the eye, and the accurate segmentation of choroidal vessels from optical coherence tomography (OCT) images provides important support for the quantitative analysis of choroid-related diseases and the development of treatment plans. Although deep learning-based methods have great potential for segmentation, these methods rely on large amounts of well-labeled data, and the data collection process is both time-consuming and laborious.\n                    <jats:italic>Approach.<\/jats:italic>\n                    In this paper, we propose a novel asymmetric semi-supervised segmentation framework called SSCR, based on a student-teacher model, to segment choroidal vessels in OCT images. The proposed framework enhances the segmentation results with uncertainty-aware self-integration and transformation consistency techniques. Meanwhile, we designed an asymmetric encoder\u2013decoder network called Pyramid Pooling SegFormer (APP-SFR) for choroidal vascular segmentation. The network combines local attention and global attention information to improve the model\u2019s ability to learn complex vascular features. Additionally, we proposed a boundary repair module that enhances boundary confidence by utilizing a repair head to re-predict selected fuzzy points and further refines the segmentation boundary.\n                    <jats:italic>Main results.<\/jats:italic>\n                    We conducted extensive experiments on three different datasets: the ChorVessel dataset with 400 OCT images, the Meibomian Glands (MG) dataset with 400 images, and the U2OS Cell Nucleus Dataset with 200 images. The proposed method achieved an average Dice score of 74.23% on the ChorVessel dataset, which is 2.95% higher than the fully supervised network (U-Net) and outperformed other comparison methods. In both the MG dataset and the U2OS cell nucleus dataset, our proposed SSCR method achieved average Dice scores of 80.10% and 87.26%, respectively.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The experimental results show that our proposed methods achieve better segmentation accuracy than other state-of-the-art methods. The method is designed to help clinicians make rapid diagnoses of ophthalmic diseases and has potential for clinical application.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad0d42","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T17:42:51Z","timestamp":1700156571000},"page":"245005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi-supervised contrast learning-based segmentation of choroidal vessel in optical coherence tomography images"],"prefix":"10.1088","volume":"68","author":[{"given":"Xiaoming","family":"Liu","sequence":"first","affiliation":[{"name":"Wuhan University of Science and Technology"},{"name":"Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingling","family":"Pan","sequence":"additional","affiliation":[{"name":"Wuhan University of Science and Technology"},{"name":"Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wuhan Aier Eye Hospital of Wuhan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University of Science and Technology"},{"name":"Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinshan","family":"Tang","sequence":"additional","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"pmbad0d42bib22","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0146344","type":"journal-article","article-title":"Choroidal vascularity index (CVI)-a novel optical coherence tomography parameter for monitoring patients with panuveitis?","volume":"11","author":"Agrawal","year":"2016a","journal-title":"PLoS One"},{"key":"pmbad0d42bib23","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1097\/IAE.0000000000001040","type":"journal-article","article-title":"Choroidal vascularity index in central serous chorioretinopathy","volume":"36","author":"Agrawal","year":"2016b","journal-title":"Retina"},{"key":"pmbad0d42bib27","first-page":"1597","type":"conference-proceedings","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"pmbad0d42bib31","first-page":"2613","type":"conference-proceedings","article-title":"Semi-supervised semantic segmentation with cross pseudo supervision","author":"Chen","year":"2021"},{"key":"pmbad0d42bib28","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1016\/j.ophtha.2015.01.029","type":"journal-article","article-title":"Spectral-domain optical coherence tomography angiography of choroidal neovascularization","volume":"122","author":"de Carlo","year":"2015","journal-title":"Ophthalmology"},{"key":"pmbad0d42bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105595","type":"journal-article","article-title":"PIPE-Net: a pyramidal-input-parallel-encoding network for the segmentation of corneal layer interfaces in OCT images","volume":"147","author":"Elsawy","year":"2022","journal-title":"Comput. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-08-14","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-11-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-12-08","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}