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De Leon JMS, Mangahas MAC. A 29-year-old man with bilateral megalocornea. Digit J Ophthalmol 2021; 27:26-28. [PMID: 34512207 DOI: 10.5693/djo.03.2020.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- John Mark S De Leon
- Department of Health Eye Center, East Avenue Medical Center, Quezon City, Philippines
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Devalla SK, Pham TH, Panda SK, Zhang L, Subramanian G, Swaminathan A, Yun CZ, Rajan M, Mohan S, Krishnadas R, Senthil V, De Leon JMS, Tun TA, Cheng CY, Schmetterer L, Perera S, Aung T, Thiéry AH, Girard MJA. Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning. Biomed Opt Express 2020; 11:6356-6378. [PMID: 33282495 PMCID: PMC7687952 DOI: 10.1364/boe.395934] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/17/2020] [Accepted: 08/19/2020] [Indexed: 05/06/2023]
Abstract
Recently proposed deep learning (DL) algorithms for the segmentation of optical coherence tomography (OCT) images to quantify the morphological changes to the optic nerve head (ONH) tissues during glaucoma have limited clinical adoption due to their device specific nature and the difficulty in preparing manual segmentations (training data). We propose a DL-based 3D segmentation framework that is easily translatable across OCT devices in a label-free manner (i.e. without the need to manually re-segment data for each device). Specifically, we developed 2 sets of DL networks: the 'enhancer' (enhance OCT image quality and harmonize image characteristics from 3 devices) and the 'ONH-Net' (3D segmentation of 6 ONH tissues). We found that only when the 'enhancer' was used to preprocess the OCT images, the 'ONH-Net' trained on any of the 3 devices successfully segmented ONH tissues from the other two unseen devices with high performance (Dice coefficients > 0.92). We demonstrate that is possible to automatically segment OCT images from new devices without ever needing manual segmentation data from them.
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Affiliation(s)
- Sripad Krishna Devalla
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Tan Hung Pham
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Satish Kumar Panda
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Liang Zhang
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Giridhar Subramanian
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Anirudh Swaminathan
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Chin Zhi Yun
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | | | | | | | | | - John Mark S De Leon
- Department of Health Eye Center, East Avenue Medical Center, Quezon City, Philippines
| | - Tin A Tun
- Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Nanyang Technological University, Singapore
- Department of Clinical Pharmacology, Medical University of Vienna, Austria
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria
- Institute of Clinical and Molecular Ophthalmology, Basel, Switzerland
| | - Shamira Perera
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Graduate Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Alexandre H Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Michaël J A Girard
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, 20 College Road, Singapore 169856, Singapore
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