{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:21:18Z","timestamp":1787026878991,"version":"build-2736575974"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.<\/jats:p>","DOI":"10.3389\/fnins.2021.743769","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T16:43:16Z","timestamp":1633884196000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images"],"prefix":"10.3389","volume":"15","author":[{"given":"Qingquan","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyue","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1109\/TBME.2013.2237906","article-title":"Segmentation of choroidal neovascularization in fundus fluorescein angiograms","volume":"60","author":"Abdelmoula","year":"2013","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep09269","article-title":"Quantitative analysis of retinal layers' optical intensities on 3d optical coherence tomography for central retinal artery occlusion","volume":"5","author":"Chen","year":"2015","journal-title":"Sci. Rep"},{"key":"B3","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"Chen","year":"2017","journal-title":"arXiv arXiv"},{"key":"B4","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1097\/IAE.0000000000002471","article-title":"Comparison between several optical coherence tomography angiography devices and indocyanine green angiography of choroidal neovascularization","volume":"40","author":"Corvi","year":"2020","journal-title":"Retina"},{"key":"B5","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1126\/science.1133807","article-title":"Htra1 promoter polymorphism in wet age-related macular degeneration","volume":"314","author":"DeWan","year":"2006","journal-title":"Science"},{"key":"B6","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","article-title":"Cpfnet: context pyramid fusion network for medical image segmentation","volume":"39","author":"Feng","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B7","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/S0002-9394(14)73649-9","article-title":"Age-related macular degeneration and choroidal neovascularization","volume":"115","author":"Freund","year":"1993","journal-title":"Am. J. Ophthalmol"},{"key":"B8","first-page":"3146","article-title":"Dual attention network for scene segmentation","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Fu","year":"2019"},{"key":"B9","doi-asserted-by":"publisher","first-page":"076010","DOI":"10.1117\/1.JBO.21.7.076010","article-title":"Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography","volume":"21","author":"Gao","year":"2016","journal-title":"J. Biomed. Opt"},{"key":"B10","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1016\/j.ajo.2003.09.042","article-title":"Choroidal neovascularization","volume":"137","author":"Grossniklaus","year":"2004","journal-title":"Am. J. Ophthalmol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: Context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"Huang","year":"1991","journal-title":"Science"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1016\/j.ophtha.2014.01.034","article-title":"Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration","volume":"121","author":"Jia","year":"2014","journal-title":"Ophthalmology"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"B15","article-title":"Pyramid attention network for semantic segmentation","author":"Li","year":"2018","journal-title":"arXiv arXiv"},{"key":"B16","doi-asserted-by":"publisher","first-page":"3564","DOI":"10.1364\/BOE.6.003564","article-title":"Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography","volume":"6","author":"Liu","year":"2015","journal-title":"Biomed. Opt. Express"},{"key":"B17","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Long","year":"2015"},{"key":"B18","article-title":"Attention u-net: learning where to look for the pancreas","author":"Oktay","year":"2018","journal-title":"arXiv arXiv"},{"key":"B19","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Ronneberger","year":"2015"},{"key":"B20","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/TMI.2014.2359980","article-title":"Automated 3-d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments","volume":"34","author":"Shi","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1016\/j.ophtha.2015.01.029","article-title":"Spectral-domain optical coherence tomography angiography of choroidal neovascularization","volume":"122","author":"Talisa","year":"2015","journal-title":"Ophthalmology"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1109\/TMI.2020.3048975","article-title":"Semi-supervised capsule cgan for speckle noise reduction in retinal oct images","volume":"40","author":"Wang","year":"","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B23","first-page":"1518","article-title":"Cu-segnet: corneal ulcer segmentation network","volume-title":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","author":"Wang","year":""},{"key":"B24","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00530-017-0582-5","article-title":"Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior","volume":"25","author":"Xi","year":"2019","journal-title":"Multimedia Syst"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1364\/OSAC.2.001011","article-title":"Mpb-cnn: a multi-scale parallel branch cnn for choroidal neovascularization segmentation in sd-oct images","volume":"2","author":"Zhang","year":"2019","journal-title":"OSA Continuum"},{"key":"B26","first-page":"2881","article-title":"Pyramid scene parsing network","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Zhao","year":"2017"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1109\/JBHI.2017.2702603","article-title":"Choroid neovascularization growth prediction with treatment based on reaction-diffusion model in 3-d oct images","volume":"21","author":"Zhu","year":"2017","journal-title":"IEEE J. Biomed. Health Inform"}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2021.743769\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T16:43:26Z","timestamp":1633884206000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2021.743769\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,8]]},"references-count":27,"alternative-id":["10.3389\/fnins.2021.743769"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2021.743769","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,8]]},"article-number":"743769"}}