Optical Technique, Volume. 48, Issue 3, 364(2022)
Research on segmentation method of OCT retinal image fluid
[1] [1] Marmor M F. Mechanisms of fluid accumulation in retinal edema[J]. Documenta Ophthalmologica,1999,97(3-4):239-249.
[2] [2] Huang D, Swanson E A, Lin C P, et al. Optical coherence tomography[J]. Science,1991,254(5035):1178-1181.
[3] [3] J W Yau, S L Rogers, R Kawasaki, et al. Global prevalence and major risk factors of diabetic retinopathy[J]. Diabetes Care,2012,35(3):556-564.
[4] [4] DeBuc D C. A review of algorithms for segmentation of retinal image data using optical coherence tomography[M]. USA:INTECH Open Access Publisher,2011:15-54.
[5] [5] Schmidt-Erfurth U, Sadeghipour A, Gerendas B S, et al. Artificial intelligence in retina[J]. Progress in Retinal and Eye Research,2018,67(6):1-29.
[6] [6] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature,2015,521(7553):436-444.
[7] [7] Long J, Shelhamer E, Darrell, T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651.
[8] [8] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]∥Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.Switzerland:Springer,Cham,2015:234-241.
[9] [9] Devalla S K, Renukanand P K, Sreedhar B K, et al. DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images[J]. Biomedical Optics Express,2018,9(7):3244-3265.
[10] [10] Gorgi Zadeh S, Wintergerst M W M, Wiens V, et al. CNNs enable accurate and fast segmentation of drusen in optical coherence tomography[M]∥ Switzerland: Springer,2017:65-73.
[11] [11] Venhuizen FG, van Ginneken B, Liefers B, et al. Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography[J]. Biomedical Optics Express,2018,9(4):1545.
[12] [12] Chen Z, Li D, Shen H, et al. Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration[J]. Optics and Laser Technology,2020,122(2):105830.
[13] [13] Ben-Cohen A, Mark D, Kovler I, et al. Retinal layers segmentation using fully convolutional network in OCT images[J]. RSIP Vision,2017,198(1):1-8.
[14] [14] Lu D, Heisler M, Lee S, et al. Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network[J]. Medical Image Analysis,2019,54(4):100-110.
[15] [15] Roy A G, Conjeti S, Karri S P K, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks[J]. Biomedical Optics Express,2017,8(8):3627-3642.
[16] [16] Z Gu, J Cheng, H Fu, et al. CE-net: Context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging,2019,38(10):2281-2292.
[17] [17] Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database[C]∥Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE,2009:248-255.
[18] [18] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA: IEEE,2016:770-778.
[19] [19] Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. Unet++: A nested u-net architecture for medical image segmentation[C]∥ Deep learning in medical image analysis and multimodal learning for clinical decision support. Switzerland:Springer,Cham,2018:3-11.
[20] [20] Lee C Y, Xie S, Gallagher P, et al. Deeply-supervised nets[C]∥ Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. San Diego:PMLR,2015:562-570.
[21] [21] Fu H, Cheng J, Xu Y, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation[J]. IEEE Transactions on Medical Imaging,2018,37(7):1597-1605.
[22] [22] Abraham N, Khan N M. A novel focal tversky loss function with improved attention u-net for lesion segmentation[C]∥Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging(ISBI 2019), Venice,Italy:IEEE,2019:683-687.
[23] [23] Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach,USA:IEEE,2019:3146-3154.
[24] [24] Liu W, Sun Y, Ji Q. MDAN-UNet: multi-scale and dual attention enhanced nested u-net architecture for segmentation of optical coherence tomography images[J]. Algorithms,2020,13(3):60.
[25] [25] Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).USA:IEEE,2020:11534-11542.
[26] [26] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[27] [27] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake city,USA:IEEE,2018:7132-7141.
[28] [28] Bogunovic' H, Venhuizen F, Klimscha S, et al. RETOUCH: The retinal OCT fluid detection and segmentation benchmark and challenge[J]. IEEE Transactions on Medical Imaging,2019,38(8):1858-1874.
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WANG Teng, CHEN Minghui, KE Shuting, YUAN yuan, LAI xiangling, HUANG Duowen, LIU Duxin, MA Xinhong. Research on segmentation method of OCT retinal image fluid[J]. Optical Technique, 2022, 48(3): 364
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Received: Jan. 7, 2022
Accepted: --
Published Online: Jan. 20, 2023
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CSTR:32186.14.