{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:44:39Z","timestamp":1782373479947,"version":"3.54.5"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1104600"],"award-info":[{"award-number":["2017YFC1104600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013069","name":"Leading Talents Program of Guangdong Province","doi-asserted-by":"publisher","award":["2016LJ06Y375"],"award-info":[{"award-number":["2016LJ06Y375"]}],"id":[{"id":"10.13039\/501100013069","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,23]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation), and lens diseases. The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise; however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.<\/jats:p>","DOI":"10.1093\/pcmedi\/pbab009","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T04:12:05Z","timestamp":1619583125000},"page":"85-92","source":"Crossref","is-referenced-by-count":10,"title":["EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations"],"prefix":"10.1093","volume":"4","author":[{"given":"Wenjia","family":"Cai","sequence":"first","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Ophthalmology, Capital Medical University, Beijing Tongren Hospital, Beijing 100730, 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