{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T17:54:21Z","timestamp":1781978061630,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T00:00:00Z","timestamp":1654300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013153","name":"Shantou Science and Technology Project","doi-asserted-by":"publisher","award":["190917085269835"],"award-info":[{"award-number":["190917085269835"]}],"id":[{"id":"10.13039\/501100013153","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013153","name":"Shantou Science and Technology Project","doi-asserted-by":"publisher","award":["NTF20021"],"award-info":[{"award-number":["NTF20021"]}],"id":[{"id":"10.13039\/501100013153","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013153","name":"Shantou Science and Technology Project","doi-asserted-by":"publisher","award":["2022A1515011396"],"award-info":[{"award-number":["2022A1515011396"]}],"id":[{"id":"10.13039\/501100013153","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013153","name":"Shantou Science and Technology Project","doi-asserted-by":"publisher","award":["180917144960530"],"award-info":[{"award-number":["180917144960530"]}],"id":[{"id":"10.13039\/501100013153","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science Research Startup Foundation of Shantou University","award":["190917085269835"],"award-info":[{"award-number":["190917085269835"]}]},{"name":"the Science Research Startup Foundation of Shantou University","award":["NTF20021"],"award-info":[{"award-number":["NTF20021"]}]},{"name":"the Science Research Startup Foundation of Shantou University","award":["2022A1515011396"],"award-info":[{"award-number":["2022A1515011396"]}]},{"name":"the Science Research Startup Foundation of Shantou University","award":["180917144960530"],"award-info":[{"award-number":["180917144960530"]}]},{"name":"Guangdong Natural Science Foundation","award":["190917085269835"],"award-info":[{"award-number":["190917085269835"]}]},{"name":"Guangdong Natural Science Foundation","award":["NTF20021"],"award-info":[{"award-number":["NTF20021"]}]},{"name":"Guangdong Natural Science Foundation","award":["2022A1515011396"],"award-info":[{"award-number":["2022A1515011396"]}]},{"name":"Guangdong Natural Science Foundation","award":["180917144960530"],"award-info":[{"award-number":["180917144960530"]}]},{"name":"the Science and Technology Planning Project of Guangdong Province of China","award":["190917085269835"],"award-info":[{"award-number":["190917085269835"]}]},{"name":"the Science and Technology Planning Project of Guangdong Province of China","award":["NTF20021"],"award-info":[{"award-number":["NTF20021"]}]},{"name":"the Science and Technology Planning Project of Guangdong Province of China","award":["2022A1515011396"],"award-info":[{"award-number":["2022A1515011396"]}]},{"name":"the Science and Technology Planning Project of Guangdong Province of China","award":["180917144960530"],"award-info":[{"award-number":["180917144960530"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Automatic and accurate estimation of choroidal thickness plays a very important role in a computer-aided system for eye diseases. One of the most common methods for automatic estimation of choroidal thickness is segmentation-based methods, in which the boundaries of the choroid are first detected from optical coherence tomography (OCT) images. The choroidal thickness is then computed based on the detected boundaries. A shortcoming in the segmentation-based methods is that the estimating precision greatly depends on the segmentation results. To avoid the dependence on the segmentation step, in this paper, we propose a direct method based on convolutional neural networks (CNNs) for estimating choroidal thickness without segmentation. Concretely, a B-scan image is first cropped into several patches. A trained CNN model is then used to estimate the choroidal thickness for each patch. The mean thickness of the choroid in the B-scan is obtained by taking the average of the choroidal thickness on each patch. Then, 150 OCT volumes are collected to evaluate the proposed method. The experiments show that the results obtained by the proposed method are very competitive with those obtained by segmentation-based methods, which indicates that direct estimation of choroidal thickness is very promising.<\/jats:p>","DOI":"10.3390\/jcm11113203","type":"journal-article","created":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T09:42:32Z","timestamp":1654335752000},"page":"3203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Direct Estimation of Choroidal Thickness in Optical Coherence Tomography Images with Convolutional Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Yibiao","family":"Rong","sequence":"first","affiliation":[{"name":"College of Engineering, Shantou University, Shantou 515063, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehua","family":"Jiang","sequence":"additional","affiliation":[{"name":"Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou 515051, China"},{"name":"Medical College, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Engineering, Shantou University, Shantou 515063, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Engineering, Shantou University, Shantou 515063, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuliang","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Engineering, Shantou University, Shantou 515063, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhun","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Engineering, Shantou University, Shantou 515063, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0676-4610","authenticated-orcid":false,"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou 515051, China"},{"name":"Medical College, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1016\/j.ajo.2008.12.008","article-title":"A pilot study of enhanced depth imaging optical coherence tomography of the choroid in normal eyes","volume":"147","author":"Margolis","year":"2009","journal-title":"Am. J. Ophthalmol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1097\/IAE.0b013e31824453ac","article-title":"Segregation of ophthalmoscopic characteristics according to choroidal thickness in patients with early age-related macular degeneration","volume":"32","author":"Switzer","year":"2012","journal-title":"Retina"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7646","DOI":"10.1167\/iovs.15-17137","article-title":"Macular choroidal thickness in children: The Shandong children eye study","volume":"56","author":"Zhang","year":"2015","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.3109\/02713683.2014.952828","article-title":"Effects of sex and age on the normal retinal and choroidal structures on optical coherence tomography","volume":"40","author":"Ooto","year":"2015","journal-title":"Curr. Eye Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1097\/IAE.0000000000002022","article-title":"Repeatability of choroidal thickness measurements assessed with swept-source optical coherence tomography in healthy and diabetic individuals","volume":"39","author":"Calvo","year":"2019","journal-title":"Retina"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/RBME.2018.2798701","article-title":"A survey of graph cuts\/graph search based medical image segmentation","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1167\/iovs.12-10578","article-title":"Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans","volume":"54","author":"Hu","year":"2013","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1364\/BOE.4.000397","article-title":"Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images","volume":"4","author":"Jing","year":"2013","journal-title":"Biomed. Opt. Express"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8974","DOI":"10.1364\/OE.23.008974","article-title":"Automated choroid segmentation based on gradual intensity distance in HD-OCT images","volume":"23","author":"Chen","year":"2015","journal-title":"Opt. Express"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"479268","DOI":"10.1155\/2014\/479268","article-title":"Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts","volume":"2014","author":"Danesh","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mao, X., Zhao, Y., Chen, B., Ma, Y., Gu, Z., Gu, S., Yang, J., Cheng, J., and Liu, J. (2020, January 20\u201324). Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Virtually, Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175631"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3408","DOI":"10.1109\/JBHI.2020.3023144","article-title":"Automatic segmentation and visualization of choroid in OCT with knowledge infused deep learning","volume":"24","author":"Zhang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_18","unstructured":"Cheng, X., Chen, X., Ma, Y., Zhu, W., and Shi, F. (2019). Medical Imaging 2019: Image Processing, SPIE."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13298","DOI":"10.1038\/s41598-019-49816-4","article-title":"Automatic choroidal segmentation in OCT images using supervised deep learning methods","volume":"9","author":"Kugelman","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.neucom.2017.01.023","article-title":"Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks","volume":"237","author":"Sui","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TBME.2014.2299433","article-title":"Direct estimation of cardiac biventricular volumes with an adapted Bayesian formulation","volume":"61","author":"Wang","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.media.2015.07.003","article-title":"Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation","volume":"30","author":"Zhen","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1109\/TBME.2017.2762762","article-title":"Multi-views fusion CNN for left ventricular volumes estimation on cardiac MR images","volume":"65","author":"Luo","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1515\/cdbme-2017-0022","article-title":"Deep convolutional neural network approach for forehead tissue thickness estimation","volume":"3","author":"Manit","year":"2017","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4753","DOI":"10.1109\/TIP.2017.2721106","article-title":"Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1109\/TMI.2019.2899534","article-title":"Direct automatic coronary calcium scoring in cardiac and chest CT","volume":"38","author":"Wolterink","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1038\/s41598-019-57196-y","article-title":"Quantification of retinal nerve fibre layer thickness on optical coherence tomography with a deep learning segmentation-free approach","volume":"10","author":"Mariottoni","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2015, January 26\u201330). Matconvnet: Convolutional Neural Networks for Matlab. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2807412"},{"key":"ref_29","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1109\/JBHI.2017.2691715","article-title":"A novel continuous blood pressure estimation approach based on data mining techniques","volume":"21","author":"Miao","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","first-page":"20","article-title":"The need for uncertainty quantification in machine-assisted medical decision making","volume":"1","author":"Begoli","year":"2019","journal-title":"J. Nat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e1424","DOI":"10.1002\/widm.1424","article-title":"Explainable artificial intelligence: An analytical review","volume":"11","author":"Angelov","year":"2021","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/11\/3203\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:24:27Z","timestamp":1760138667000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/11\/3203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,4]]},"references-count":33,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["jcm11113203"],"URL":"https:\/\/doi.org\/10.3390\/jcm11113203","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,4]]}}}