Journal of Innovative Optical Health Sciences, Volume. 17, Issue 6, 2450021(2024)
Retinal layer segmentation using gradient feature calculation in OCT
[1] D. Huang et al. Optical coherence tomography. Science, 254, 1178-1181(1991).
[2] H. Bian et al. GPU-accelerated image registration algorithm in ophthalmic optical coherence tomography. Biomed. Opt. Express, 14, 194-207(2022).
[3] Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, C. Y. Cheng. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology, 121, 2081-2090(2014).
[4] A. P. Yow et al. Automatic visual impairment detection system for age-related eye diseases through gaze analysis. 2017 39th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), 2450-2453(2017).
[5] K. Ogurtsova et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract., 128, 40-50(2017).
[6] G. Virgili et al. Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. Cochrane Database Syst. Rev., 1, CD008081(2015).
[7] S. Moghimi et al. Macular and optic nerve head vessel density and progressive retinal nerve fiber layer loss in glaucoma. Ophthalmology, 125, 1720-1728(2018).
[8] R. L. W. Hanson, A. Airody, S. Sivaprasad, R. P. Gale. Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: A systematic review. Eye, 37, 2438-2453(2023).
[9] W. Du, X. Tian, Y. Sun. A dynamic threshold segmentation algorithm for anterior chamber OCT images based on wavelet transform. 2012 5th Int. Congr. Image and Signal Processing, 279-282(2012).
[10] A. M. Bagci, M. Shahidi, R. Ansari, M. Blair, N. P. Blair, R. Zelkha. Thickness profiles of retinal layers by optical coherence tomography image segmentation. Am. J. Ophthalmol., 146, 679-687(2008).
[11] B. Gimi et al. Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881Q(2016).
[12] K. Gawlik, F. Hausser, F. Paul, A. U. Brandt, E. M. Kadas. Active contour method for ILM segmentation in ONH volume scans in retinal OCT. Biomed. Opt. Express, 9, 6497-6518(2018).
[13] D. Sayers, M. S. Habib, B. Al-Diri. Manual tool and semi-automated graph theory method for layer segmentation in optical coherence tomography. Intelligent Computing: Proc. 2019 Computing Conf., 1, 1090-1109(2019).
[14] G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abramoff, M. Sonka. Three-dimensional analysis of retinal layer texture: Identification of fluid-filled regions in SD-OCT of the macula. IEEE Trans. Med. Imaging, 29, 1321-1330(2010).
[15] J. Liu et al. Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator. Sci. Rep., 12, 1412(2022).
[16] A. Mishra, A. Wong, K. Bizheva, D. A. Clausi. Intra-retinal layer segmentation in optical coherence tomography images. Opt. Express, 17, 23719-23728(2009).
[17] S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, S. Farsiu. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express, 18, 19413-19428(2010).
[18] D. M. Greig, B. T. Porteous, A. H. Seheult. Exact maximum a posteriori estimation for binary images. J. R. Stat. Soc. B, Stat. Methodol., 51, 271-279(1989).
[19] K. A. Vermeer, J. van der Schoot, H. G. Lemij, J. F. de Boer. Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images. Biomed. Opt. Express, 2, 1743-1756(2011).
[20] A. Lang et al. Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Opt. Express, 4, 1133-1152(2013).
[21] J. Kugelman, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, M. J. Collins. Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search. Biomed. Opt. Express, 9, 5759-5777(2018).
[22] V. Kajic et al. Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Opt. Express, 18, 14730-14744(2010).
[23] J. Kugelman et al. A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation. Sci. Rep., 12, 14888(2022).
[24] S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, S. Farsiu. Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed. Opt. Express, 6, 1172-1194(2015).
[25] J. Tian, B. Varga, G. M. Somfai, W. H. Lee, W. E. Smiddy, D. C. DeBuc. Real-time automatic segmentation of optical coherence tomography volume data of the macular region. PLoS One, 10, e0133908(2015).
[26] L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu. Sparsity based denoising of spectral domain optical coherence tomography images. Biomed. Opt. Express, 3, 927-942(2012).
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Lei Liu, Yeman Liu, Xiaoteng Yan, Haiyi Bian, Hang Xu, Chunzhong Li, Hongnan Duan. Retinal layer segmentation using gradient feature calculation in OCT[J]. Journal of Innovative Optical Health Sciences, 2024, 17(6): 2450021
Category: Research Articles
Received: May. 25, 2024
Accepted: Aug. 6, 2024
Published Online: Nov. 13, 2024
The Author Email: Haiyi Bian (bianhaiyi@163.com)