{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:12:57Z","timestamp":1774627977969,"version":"3.50.1"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2022,1,31]]},"abstract":"<jats:p>\n            Early detection and treatment of glaucoma is of interest as it is a chronic eye disease leading to an irreversible loss of vision. Existing automated systems rely largely on fundus images for assessment of glaucoma due to their fast acquisition and cost-effectiveness.\n            <jats:bold>Optical Coherence Tomographic<\/jats:bold>\n            (\n            <jats:bold>OCT<\/jats:bold>\n            ) images provide vital and unambiguous information about nerve fiber loss and optic cup morphology, which are essential for disease assessment. However, the high cost of OCT is a deterrent for deployment in screening at large scale. In this article, we present a novel CAD solution wherein both OCT and fundus modality images are leveraged to learn a model that can perform a mapping of fundus to OCT feature space. We show how this model can be subsequently used to detect glaucoma given an image from only one modality (fundus). The proposed model has been validated extensively on four public andtwo private datasets. It attained an AUC\/Sensitivity value of 0.9429\/0.9044 on a diverse set of 568 images, which is superior to the figures obtained by a model that is trained only on fundus features. Cross-validation was also done on nearly 1,600 images drawn from a private (OD-centric) and a public (macula-centric) dataset and the proposed model was found to outperform the state-of-the-art method by 8% (public) to 18% (private). Thus, we conclude that fundus to OCT feature space mapping is an attractive option for glaucoma detection.\n          <\/jats:p>","DOI":"10.1145\/3470979","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T01:39:53Z","timestamp":1634434793000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Glaucoma Assessment from Fundus Images with Fundus to OCT Feature Space Mapping"],"prefix":"10.1145","volume":"3","author":[{"given":"Divya Jyothi","family":"Gaddipati","sequence":"first","affiliation":[{"name":"International Institute of Information Technology Hyderabad, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayanthi","family":"Sivaswamy","sequence":"additional","affiliation":[{"name":"International Institute of Information Technology Hyderabad, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"669","article-title":"Automatic feature learning for glaucoma detection based on deep learning","author":"Chen X.","year":"2015","unstructured":"X. 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In EMBC, 715\u2013718.","journal-title":"EMBC"},{"key":"e_1_3_1_5_2","first-page":"2775","article-title":"G-Eyenet: A convolutional autoencoding classifier framework for the detection of glaucoma from retinal fundus images","author":"Pal A.","year":"2018","unstructured":"A. Palet al. 2018. G-Eyenet: A convolutional autoencoding classifier framework for the detection of glaucoma from retinal fundus images. In ICIP, 2775\u20132779.","journal-title":"ICIP"},{"key":"e_1_3_1_7_2","volume-title":"IEEE Transactions on Medical Imaging","author":"Diaz-Pinto A.","year":"2019","unstructured":"A. Diaz-Pintoet al. 2019. Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Transactions on Medical Imaging 38, 9 (2019), 2211\u20132218. 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Conditional generative adversarial nets. arXiv:1411.1784. https:\/\/arxiv.org\/abs\/1411.1784.","journal-title":"arXiv:1411.1784"},{"key":"e_1_3_1_31_2","first-page":"53","article-title":"Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation","author":"Sivaswamy J.","year":"2014","unstructured":"J. Sivaswamyet al. 2014. Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation. In ISBI, 53\u201356.","journal-title":"ISBI"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CBMS.2011.5999143"},{"key":"e_1_3_1_33_2","unstructured":"REFUGE. 2018. Retinal Fundus Glaucoma Challenge. Retrieved 2019 from https:\/\/refuge.grand-challenge.org\/Home."},{"key":"e_1_3_1_34_2","article-title":"sjchoi86-HRF","year":"2016","unstructured":"Sungjoon. 2016. sjchoi86-HRF. 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