{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T16:36:42Z","timestamp":1792427802534,"version":"4.5.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010246","name":"Postdoctoral Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010246","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010242","name":"Jiangsu Planned Projects for Postdoctoral Research Funds","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>Pterygium is a common ocular surface disease. When pterygium significantly invades the cornea, it limits eye movement and impairs vision, which requires surgery to remove. It is medically recognized that when the width of the pterygium that invades the cornea is &amp;gt;3 mm, the patient can be treated with surgical resection. Owing to this, this study proposes a system for diagnosing and measuring the pathological progress of pterygium using deep learning methods, which aims to assist doctors in designing pterygium surgical treatment strategies. The proposed system only needs to input the anterior segment images of patients to automatically and efficiently measure the width of the pterygium that invades the cornea, and the patient's pterygium symptom status can be obtained. The system consists of three modules, including cornea segmentation module, pterygium segmentation module, and measurement module. Both segmentation modules use convolutional neural networks. In the pterygium segmentation module, to adapt the diversity of the pterygium's shape and size, an improved U-Net++ model by adding an Attention gate before each up-sampling layer is proposed. The Attention gates extract information related to the target, so that the model can pay more attention to the shape and size of the pterygium. The measurement module realizes the measurement of the width and area of the pterygium that invades the cornea and the classification of pterygium symptom status. In this study, the effectiveness of the proposed system is verified using datasets collected from the ocular surface diseases center at the Affiliated Eye Hospital of Nanjing Medical University. The results obtained show that the Dice coefficient of the cornea segmentation module and the pterygium segmentation module are 0.9620 and 0.9020, respectively. The Kappa consistency coefficient between the final measurement results of the system and the doctor's visual inspection results is 0.918, which proves that the system has practical application significance.<\/jats:p>","DOI":"10.3389\/fmed.2022.819971","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T04:41:27Z","timestamp":1644813687000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A Novel System for Measuring Pterygium's Progress Using Deep Learning"],"prefix":"10.3389","volume":"9","author":[{"given":"Cheng","family":"Wan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwei","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenghu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaona","family":"Jing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.18240\/ijo.2019.04.01","article-title":"Identification of pathogenic genes of pterygium based on the gene expression omnibus database","volume":"12","author":"Yue","year":"2019","journal-title":"Int J Ophthalmol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"903","DOI":"10.18240\/ijo.2021.06.17","article-title":"Bibliometric analysis and mapping knowledge domain of pterygium: 2000\u20132019","volume":"14","author":"Wang","year":"2021","journal-title":"Int J Ophthalmol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1167\/iovs.11-7941","article-title":"Involvement of SPARC and MMP-3 in the pathogenesis of human pterygium","volume":"53","author":"Seet","year":"2012","journal-title":"Invest Ophthalmol Vis Sci."},{"key":"B4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.exer.2015.01.010","article-title":"Fibroblast biology in pterygia","volume":"142","author":"Kim","year":"2016","journal-title":"Exp Eye Res."},{"key":"B5","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s10792-017-0514-6","article-title":"Corneal tomography and biomechanics in primary pterygium","volume":"38","author":"Vanathi","year":"2018","journal-title":"Int Ophthalmol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"e0171954","DOI":"10.1371\/journal.pone.0171954","article-title":"The incidence and prevalence of pterygium in South Korea: A 10-year population-based Korean cohort study","volume":"12","author":"Rim","year":"2017","journal-title":"PLoS ONE."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1001\/archopht.1997.01100160405001","article-title":"Effect of pterygium morphology on pterygium recurrence in a controlled trial comparing conjunctival autografting with bare sclera excision","volume":"115","author":"Tan","year":"1997","journal-title":"Arch Ophthalmol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"4434","DOI":"10.1109\/EMBC.2012.6346950","article-title":"Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system","volume":"2012","author":"Gao","year":"2012","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc."},{"key":"B9","first-page":"229","volume-title":"Pterygium Tissues Segmentation Using Densely Connected DeepLab 10th Symposium on Computer Applications & Industrial Electronics 2020","author":"Abdani","year":"2020"},{"key":"B10","unstructured":"Iris segmentation method of pterygium anterior segment photographed image6972\n            AbdaniSR\n            ZakiW\n            Mimi DiyanaW\n            MustaphaA\n            HussainA\n          10.1109\/ISCAIE.2015.729833027295638Electronics.20152015"},{"key":"B11","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/ELECSYM.2015.7380813","article-title":"An adaptive nonlinear enhancement method using sigmoid function for iris segmentation in pterygium cases","volume":"2015","author":"Abdani","year":"2015","journal-title":"Int Elect Symposium"},{"key":"B12","first-page":"234","volume-title":"U-net: Convolutional Networks for Biomedical Image Segmentation. 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