Expanding the analysis of optical coherence tomography images_Chinese Journal of Ocular Fundus Diseases

Authors：

 Liu Qinghuai , Hu Zizhong

Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China;

Corresponding author：

Liu Qinghuai, Email: liuqh@njmu.edu.cn

Keywords：

Tomography, optical coherence; Image analysis; Artificial intelligence; Editorial

DOI：

10.3760/cma.j.cn511434-20221010-00537

Video：

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Optical coherence tomography (OCT), as a high-resolution, non-invasive, in-vivo image method has been widely used in retinal field, especially in the examination of fundus diseases. Nowadays, the modality has been gradually popularized in most of the national basic-level hospitals. However, OCT is only employed as a diagnostic tool in most cases, ophthalmologists lack of awareness of further exploring the information behind the raw data. In the era of fast-developing artificial intelligence, on the basis of standardized information management, a more comprehensive OCT database should be established. Further original image processing, lesion analysis, and artificial intelligence development of OCT images will help improve the understanding level of vitreoretinal diseases among clinicians and assist ophthalmologists to make more appropriate clinical decisions.

Citation： Liu Qinghuai, Hu Zizhong. Expanding the analysis of optical coherence tomography images. Chinese Journal of Ocular Fundus Diseases, 2022, 38(11): 873-875. doi: 10.3760/cma.j.cn511434-20221010-00537 Copy

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1. He Y, Chen X, Tsui I, et al. Insights into the developing fovea revealed by imaging[J/OL]. Prog Retin Eye Res, 2022, 90: 101067[2022-05-17]. https://pubmed.ncbi.nlm.nih.gov/35595637/. DOI: 10.1016/j.preteyeres.2022.101067.
2. Hood DC, La Bruna S, Tsamis E, et al. Detecting glaucoma with only OCT: implications for the clinic, research, screening, and AI development[J/OL]. Prog Retin Eye Res, 2022, 90: 101052[2022-02-23]. https://pubmed.ncbi.nlm.nih.gov/35216894/. DOI: 10.1016/j.preteyeres.2022.101052.
3. 陆方, 佘凯芩, 梁莉聪. 充分发挥光相干断层扫描及其血管成像的临床应用价值, 不断提升神经眼科疾病的诊治水平[J]. 中华眼底病杂志, 2021, 37(3): 169-172. DOI: 10.3760/cma.j.cn511434-20210316-00139.Lu F, She KQ, Liang LC. Applying optical coherence tomography and optical coherence tomography angiography to improve the diagnosis and treatment of neuro-ophthalmic diseases[J]. Chin J Ocul Fundus Dis, 2021, 37(3): 169-172. DOI: 10.3760/cma.j.cn511434-20210316-00139.
4. Fujimoto J, Swanson E. The development, commercialization, and impact of optical coherence tomography[J]. Invest Ophthalmol Vis Sci, 2016, 57(9): 1-13. DOI: 10.1167/iovs.16-19963.
5. Duker JS, Kaiser PK, Binder S, et al. The International Vitreomacular Traction Study Group classification of vitreomacular adhesion, traction, and macular hole[J]. Ophthalmology, 2013, 120(12): 2611-2619. DOI: 10.1016/j.ophtha.2013.07.042.
6. Stevenson W, Prospero Ponce CM, Agarwal DR, et al. Epiretinal membrane: optical coherence tomography-based diagnosis and classification[J]. Clin Ophthalmol, 2016, 10: 527-534. DOI: 10.2147/OPTH.S97722.
7. Ruiz-Medrano J, Montero JA, Flores-Moreno I, et al. Myopic maculopathy: current status and proposal for a new classification and grading system (ATN)[J]. Prog Retin Eye Res, 2019, 69: 80-115. DOI: 10.1016/j.preteyeres.2018.10.005.
8. Wu M, Chen W, Chen Q, et al. Noise reduction for SD-OCT using a structure-preserving domain transfer approach[J]. IEEE J Biomed Health Inform, 2021, 25(9): 3460-3472. DOI: 10.1109/JBHI.2021.3071421.
9. Chen Q, Niu S, Yuan S, et al. High-low reflectivity enhancement based retinal vessel projection for SD-OCT images[J/OL]. Med Phys, 2016, 43(10): 5464[2016-10-01]. https://pubmed.ncbi.nlm.nih.gov/27782707/. DOI: 10.1118/1.4962470.
10. Chen Q, Leng T, Niu S, et al. A false color fusion strategy for drusen and geographic atrophy visualization in optical coherence tomography images[J]. Retina, 2014, 34(12): 2346-2358. DOI: 10.1097/IAE.0000000000000249.
11. Li M, Chen Y, Ji Z, et al. Image projection network: 3D to 2D image segmentation in OCTA images[J]. IEEE Trans Med Imaging, 2020, 39(11): 3343-3354. DOI: 10.1109/TMI.2020.2992244.
12. Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5): 1122-1131. DOI: 10.1016/j.cell.2018.02.010.
13. Li MC, Wang YX, Ji ZX, et al. A fast and robust fovea detectionframework for oct images based on fovealavascular zone segmentation[J/OL]. OSA Continuum 2020, 3(3): 214575452[2020-03-15]. https://www.semanticscholar.org/paper/Fast-and-robust-fovea-detection-framework-for-OCT-Li-Wang/f8bd9b24e39a3be08bfb60f5c36ec9c807acd2a0. DOI: 10.1364/osac.381120.
14. Yu C, Xie S, Niu S, et al. Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks[J]. Med Phys, 2019, 46(10): 4502-4519. DOI: 10.1002/mp.13728.
15. Wu M, Cai X, Chen Q, et al. Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging[J/OL]. Comput Methods Programs Biomed, 2019, 182: 105101[2019-09-28]. https://pubmed.ncbi.nlm.nih.gov/31600644/. DOI: 10.1016/j.cmpb.2019.105101.
16. Chen Q, Niu S, Fang W, et al. Automated choroid segmentation of three-dimensional SD-OCT images by incorporating EDI-OCT images[J]. Comput Methods Programs Biomed, 2018, 158: 161-171. DOI: 10.1016/j.cmpb.2017.11.002.
17. Niu S, Yu C, Chen Q, et al. Multimodality analysis of hyper-reflective foci and hard exudates in patients with diabetic retinopathy[J/OL]. Sci Rep, 2017, 7(1): 1568[2017-05-08]. https://pubmed.ncbi.nlm.nih.gov/28484225/. DOI: 10.1038/s41598-017-01733-0.
18. Bogunovic H, Waldstein SM, Schlegl T, et al. Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach[J]. Invest Ophthalmol Vis Sci, 2017, 58(7): 3240-3248. DOI: 10.1167/iovs.16-21053.

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