{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:09:25Z","timestamp":1787310565588,"version":"build-2736575974"},"reference-count":51,"publisher":"American Association for the Advancement of Science (AAAS)","funder":[{"DOI":"10.13039\/501100018609","name":"High-level Hospital Construction Project of Guangdong Provincial People's Hospital","doi-asserted-by":"publisher","award":["BMU2024GJJXK004"],"award-info":[{"award-number":["BMU2024GJJXK004"]}],"id":[{"id":"10.13039\/501100018609","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82371112"],"award-info":[{"award-number":["82371112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["623B2001"],"award-info":[{"award-number":["623B2001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62394311"],"award-info":[{"award-number":["62394311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Natural Science Foundation","award":["Z210008"],"award-info":[{"award-number":["Z210008"]}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Health Data Sci"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>\n            <jats:bold>Background:<\/jats:bold>\n            The choroid is the most vascularized structure in the human eye, associated with numerous retinal and choroidal diseases. However, the vessel distribution of choroidal sublayers has yet to be effectively explored due to the lack of suitable tools for visualization and analysis.\n            <jats:bold>Methods:<\/jats:bold>\n            In this paper, we present a novel choroidal angiography strategy to more effectively evaluate vessels within choroidal sublayers in the clinic. Our approach utilizes a segmentation model to extract choroidal vessels from OCT B-scans layer by layer. Furthermore, we ensure that the model, trained on B-scans with high choroidal quality, can proficiently handle the low-quality B-scans commonly collected in clinical practice for reconstruction vessel distributions. By treating this process as a cross-domain segmentation task, we propose an ensemble discriminative mean teacher structure to address the specificities inherent in this cross-domain segmentation process. The proposed structure can select representative samples with minimal label noise for self-training and enhance the adaptation strength of adversarial training.\n            <jats:bold>Results:<\/jats:bold>\n            Experiments demonstrate the effectiveness of the proposed structure, achieving a dice score of 77.28 for choroidal vessel segmentation. This validates our strategy to provide satisfactory choroidal angiography noninvasively, supportting the analysis of choroidal vessel distribution for paitients with choroidal diseases. We observed that patients with central serous chorioretinopathy have evidently (\n            <jats:italic>P<\/jats:italic>\n            \u00a0&lt;\u00a00.05) lower vascular indexes at all choroidal sublayers than healthy individuals, especially in the region beyond central fovea of macula (larger than 6\u00a0mm).\n            <jats:bold>Conclusions:<\/jats:bold>\n            We release the code and training set of the proposed method as the first noninvasive mechnism to assist clinical application for the analysis of choroidal vessels.\n          <\/jats:p>","DOI":"10.34133\/hds.0170","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T17:20:22Z","timestamp":1719940822000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":6,"title":["Choroidal Optical Coherence Tomography Angiography: Noninvasive Choroidal Vessel Analysis via Deep 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Vol. 3 p. 896."},{"key":"e_1_3_5_27_2","doi-asserted-by":"crossref","unstructured":"Chen X Yuan Y Zeng G Wang J. Semi-supervised semantic segmentation with cross pseudo supervision. arXiv. 2021. https:\/\/doi.org\/10.48550\/arXiv.2106.01226","DOI":"10.1109\/CVPR46437.2021.00264"},{"issue":"8","key":"e_1_3_5_28_2","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","article-title":"Virtual adversarial training: A regularization method for supervised and semi-supervised learning","volume":"41","author":"Miyato T","year":"2018","unstructured":"Miyato T, Maeda S-I, Koyama M, Ishii S. Virtual adversarial training: A regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell. 2018;41(8):1979\u20131993.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"e_1_3_5_29_2","doi-asserted-by":"crossref","unstructured":"Ouali Y Hudelot C Tami M. 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Realistic evaluation of semi-supervised learning algortihms. In; International Conference on Learning Representations. 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