山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (11): 33-38.doi: 10.6040/j.issn.1671-7554.0.2020.1136
摘要:
随着人口老龄化的日渐严重,眼科疾病的相关研究已成为重要的公共卫生课题。医学影像是眼科疾病临床诊断的重要辅助工具。近年来,随着人工智能技术的发展,利用人工智能技术结合医学影像对眼科疾病进行自动诊断已成为一个热门的研究课题。本文对眼科疾病智能诊断方法的最新进展进行综述,从影像中的病灶自动分割和眼科疾病的智能诊断两方面对现有方法进行分析,并对未来的研究方向进行展望,有利于为眼科智能诊断提供新观点和新思路。
中图分类号:
1 |
Kanagasingam Y , Bhuiyan A , Abràmoff MD , et al. Progress on retinal image analysis for age related macular degeneration[J]. Prog Retin Eye Res, 2014, 38: 20- 42.
doi: 10.1016/j.preteyeres.2013.10.002 |
2 |
Acott TS , Vranka JA , Keller KE , et al. Progress in retinal and eye research normal and glaucomatous outflow regulation[J]. Prog Retin Eye Res, 2020, 11: 100897.
doi: 10.1016/j.preteyeres.2020.100897 |
3 | Ahmed SF, Walid MA, Ahmed EM, Segmentation of Choroidal Neovascularization Lesions in Fluorescein Angiograms Using Parametric Modeling of the Intensity Variation[C]. IEEE Trans Biomed Eng, 2011: 1457-1460. doi: 10.1109/TBME.2013.2237906.Epub2013Jan9. |
4 | Lin KS , Tsai CL , Chen SJ , et al. Automatic Evaluation of Choroidal Neovascularization in Fluorescein Angiography[J]. Advances in Intelligent Systems and Applications, 2013, 21 (2): 377- 382. |
5 |
Tsai CL , Yang YL , Chen SJ , et al. Automatic characterization of classic choroidal neovascularization by using adaboost for supervised learning[J]. Invest Ophthalmol Vis Sci, 2011, 52 (5): 2767- 2774.
doi: 10.1167/iovs.10-6048 |
6 | Abdelmoula WM , Shah SM , Fahmy AS . Segmentation of choroidal neovascularization in fundus fluorescein angiograms[J]. IEEE Trans Biomed Eng, 2013, 60 (5): 1439- 1445. |
7 |
Gao SS , Liu L , Bailey ST , et al. Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography[J]. J Biomed Opt, 2016, 21 (7): 76010.
doi: 10.1117/1.JBO.21.7.076010 |
8 |
Tan NM , Xu Y , Goh WB , et al. Robust multi-scale superpixel classification for optic cup localization[J]. Comput Med Imaging Graph, 2015, 40: 182- 193.
doi: 10.1016/j.compmedimag |
9 | Thakur N , Juneja M . Clustering based approach for segmentation of optic cup and optic disc for detection of Glaucoma[J]. Curr Med Imaging Rev, 2017, 13 (1): 99- 105. |
10 |
Cheng J , Liu J , Xu Y , et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening[J]. IEEE Trans Med Imaging, 2013, 32 (6): 1019- 1032.
doi: 10.1109/TMI.2013.2247770 |
11 |
Girshick R , Donahue J , Darrell T , et al. Region-based convolutional networks for accurate object detection and segmentation[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38 (1): 142- 158.
doi: 10.1109/TPAMI.2015.2437384 |
12 | Dahl GE , Yu D , Deng L , et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Trans Audio Speech Lang Process, 2012, 20 (1): 30- 42. |
13 |
Mohamed AR , Dahl GE , Hinton G . Acoustic modeling using deep belief networks[J]. IEEE Trans Audio Speech Lang Process, 2012, 20 (1): 14- 22.
doi: 10.1109/TASL.2011.2109382 |
14 |
Ling ZH , Deng L , Yu D , et al. Modeling spectral envelopes using restricted Boltzmann machines and deep belief networks for statistical parametric speech synthesis[J]. IEEE Trans Audio Speech Lang Process, 2013, 21 (10): 2129- 2139.
doi: 10.1109/TASL.2013.2269291 |
15 |
Zilly J , Buhmann JM , Mahapatra D . Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation[J]. Comput Med Imaging Graph, 2017, 55: 28- 41.
doi: 10.1016/j.compmedimag |
16 | Fu HZ , Cheng J , Xu YW , et al. Joint optic disc and cup segmentation based on mufti-label deep network and polar transformation[J]. IEEE Trans Med Imaging, 2018, 37 (7): 1597- 1605. |
17 | Xie Z , Ling T , Yang Y , et al. Optic disc and cup image segmentation utilizing contour-based transformation and sequence labeling networks[J]. J Med Syst, 2020, 44 (5): 1- 13. |
18 | Jiang YM , Duan LX , Cheng J , et al. Joint RCNN: a region-based convolutional neural network for optic disc and cup segmentation[J]. IEEE Trans Biomed Eng, 2020, 67 (2): 335- 343. |
19 | Yang XB , Zhang Y . Multi-atlas segmentation of optic disc in retinal images via convolutional neural network[J]. Multimed Tools Appl, 2020, 1- 11. |
20 | Rong YB, Yu K, Xiang DH, et al. Explaining convolutional neural networks for area estimation of choroidal neovascularization via genetic programming[M]//Computational pathology and ophthalmic medical image analysis. cham: springer international publishing, 2018: 210-218. doi: 10.1007/978-3-030-00949-6_25. |
21 | Maunz A , Benmansour F , Li Y , et al. Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD)[J]. Invest Ophthalmol Vis Sci, 2020, 61 (7): 2649- 2649. |
22 | Guan L, Yu K, Chen X. Fully automated detection and quantification of multiple retinal lesions in OCT volumes based on deep learning and improved DRLSE[C]. In Medical Imaging 2019: Image Processing. 2019: 1094933. doi: 10.1117/12.2512656. |
23 | Xi XM , Meng XJ , Yang L , et al. Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior[J]. Multimed Syst, 2019, 25 (2): 95- 102. |
24 |
Gulshan V , Peng L , Coram M , et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316 (22): 2402- 2410.
doi: 10.1001/jama.2016.17216 |
25 |
Takahashi H , Tampo H , Arai Y , et al. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy[J]. PLoS One, 2017, 12 (6): e0179790.
doi: 10.1371/journal.pone.0179790 |
26 |
Shankar K , Sait ARW , Gupta D , et al. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model[J]. Pattern Recognit Lett, 2020, 133: 210- 216.
doi: 10.1016/j.patrec.2020.02.026 |
27 |
Qummar S , Khan FG , Shah S , et al. A deep learning ensemble approach for diabetic retinopathy detection[J]. IEEE Access, 2019, 7: 150530- 150539.
doi: 10.1109/access.2019.2947484 |
28 |
de la Torre J , Valls A , Puig D . A deep learning interpretable classifier for diabetic retinopathy disease grading[J]. Neurocomputing, 2020, 396: 465- 476.
doi: 10.1016/j.neucom.2018.07.102 |
29 | Alqudah AM . AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images[J]. Med Biol Eng Comput, 2020, 58 (1): 41- 53. |
30 |
Fang LY , Jin YX , Huang LF , et al. Iterative fusion convolutional neural networks for classification of optical coherence tomography images[J]. J Vis Commun Image Represent, 2019, 59: 327- 333.
doi: 10.1016/j.jvcir.2019.01.022 |
31 |
Fang L , Wang C , Li S , et al. Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification[J]. IEEE Trans Med Imaging, 2019, 38 (8): 1959- 1970.
doi: 10.1109/TMI.2019.2898414 |
32 |
Das V , Prabhakararao E , Dandapat S , et al. B-scan attentive CNN for the classification of retinal optical coherence tomography volumes[J]. IEEE Signal Process Lett, 2020, 27: 1025- 1029.
doi: 10.1109/LSP.2020.3000933 |
33 | Abbas Q . Glaucoma-deep: detection of Glaucoma eye disease on retinal fundus images using deep learning[J]. Ijacsa, 2017, 8 (6): 41- 45. |
34 |
Diaz-Pinto A , Morales S , Naranjo V , et al. Cnns for automatic glaucoma assessment using fundus images: an extensive validation[J]. BioMed Eng OnLine, 2019, 18: 29.
doi: 10.1186/s12938-019-0649-y |
35 |
Fu HZ , Cheng J , Xu YW , et al. Disc-aware ensemble network for glaucoma screening from fundus image[J]. IEEE Trans Med Imaging, 2018, 37 (11): 2493- 2501.
doi: 10.1109/TMI.2018.2837012 |
36 | Petersen CA , Mehta P , Lee AY , et al. Data-driven, feature-agnostic deep learning vs retinal nerve fiber layer thickness for the diagnosis of glaucoma[J]. JAMA Ophthalmol, 2020, 138 (4): 339- 340. |
37 |
Chai YD , Liu HY , Xu J . Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models[J]. Knowl-Based Syst, 2018, 161: 147- 156.
doi: 10.1016/j.knosys.2018.07.043 |
38 | Xu X , Zhang LL , Li JQ , et al. A hybrid global -local representation CNN model for automatic cataract grading[J]. IEEE J Biomed Health Inform, 2020, 24 (2): 556- 567. |
39 | Long EP , Lin HT , Liu ZZ , et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts[J]. Nat Biomed Eng, 2017, 1 (2): 0024. |
40 |
Gao XT , Lin S , Wong TY . Automatic feature learning to grade nuclear cataracts based on deep learning[J]. Comput Vis--ACCV 2014, 2015, 2693- 2701.
doi: 10.1007/978-3-319-16808-1_42 |
41 |
Zhang H , Niu K , Xiong Y , et al. Automatic cataract grading methods based on deep learning[J]. Comput Methods Programs Biomed, 2019, 182: 104978.
doi: 10.1016/j.cmpb.2019.07.006 |
42 |
Zhou Y , Li GQ , Li HQ . Automatic cataract classification using deep neural network with discrete state transition[J]. IEEE Trans Med Imaging, 2020, 39 (2): 436- 446.
doi: 10.1109/TMI.2019.2928229 |
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