Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phát hiện tự động điểm rò rỉ trong bệnh võng mạc tinh thể trung tâm bằng phương pháp chụp mạch huỳnh quang đáy mắt dựa trên việc học sâu theo chuỗi thời gian
Tóm tắt
Nghiên cứu này nhằm phát hiện tự động các điểm rò rỉ của bệnh võng mạc tinh thể trung tâm (CSC) từ các hình ảnh động của chụp mạch huỳnh quang đáy mắt (FFA) bằng cách sử dụng một thuật toán học sâu (DLA). Nghiên cứu bao gồm 2104 hình ảnh FFA từ 291 chuỗi FFA của 291 mắt (137 mắt phải và 154 mắt trái) từ 262 bệnh nhân. Các điểm rò rỉ được phân đoạn bằng mạng cửa chú ý (AGN). Vùng đĩa thị (OD) và vùng hoàng điểm được phân đoạn đồng thời bằng cách sử dụng U-net. Để giảm bớt số lượng dương tính giả dựa trên chuỗi thời gian, các điểm rò rỉ được đối chiếu theo vị trí của chúng liên quan đến OD và hoàng điểm. Chỉ với AGN, số lượng trường hợp có kết quả phát hiện hoàn toàn khớp với thực tế chỉ là 37 trên tổng số 61 trường hợp (60.7%) trong tập kiểm tra. Chỉ số Dice trên mức tổn thương là 0.811. Sử dụng quy trình loại bỏ để loại bỏ các dương tính giả, số lượng trường hợp phát hiện chính xác tăng lên 57 (93.4%). Chỉ số Dice trên mức tổn thương cũng cải thiện lên 0.949. Sử dụng DLA, các điểm rò rỉ CSC trong FFA có thể được xác định một cách tái lập và chính xác với sự khớp tốt với thực tế. Phát hiện mới này có thể mở đường cho ứng dụng tiềm năng của trí tuệ nhân tạo trong việc hướng dẫn liệu pháp laser.
Từ khóa
#bệnh võng mạc tinh thể trung tâm #rò rỉ #chụp mạch huỳnh quang đáy mắt #học sâu #mạng cửa chú ý #phân đoạnTài liệu tham khảo
Wong KH, Lau KP, Chhablani J, Tao Y, Li Q, Wong IY (2016) Central serous chorioretinopathy: what we have learnt so far. Acta Ophthalmol (Copenh) 94(4):321–325. https://doi.org/10.1111/aos.12779
Daruich A, Matet A, Dirani A, Bousquet E, Zhao M, Farman N, Jaisser F, Behar-Cohen F (2015) Central serous chorioretinopathy: recent findings and new physiopathology hypothesis. Prog Retin Eye Res 48:82–118. https://doi.org/10.1016/j.preteyeres.2015.05.003
Song IS, Shin YU, Lee BR (2012) Time-periodic characteristics in the morphology of idiopathic central serous chorioretinopathy evaluated by volume scan using spectral-domain optical coherence tomography. Am J Ophthalmol 154(2):366–375 e364. https://doi.org/10.1016/j.ajo.2012.02.031
Hussain N, Baskar A, Ram LSM, Das T (2006) Optical coherence tomographic pattern of fluorescein angiographic leakage site in acute central serous chorioretinopathy. Clin Experiment Ophthalmol 34(2):137–140. https://doi.org/10.1111/j.1442-9071.2006.1171.x
van Rijssen TJ, van Dijk EHC, Yzer S, Ohno-Matsui K, Keunen JEE, Schlingemann RO, Sivaprasad S, Querques G, Downes SM, Fauser S, Hoyng CB, Piccolino FC, Chhablani JK, Lai TYY, Lotery AJ, Larsen M, Holz FG, Freund KB, Yannuzzi LA, Boon CJF (2019) Central serous chorioretinopathy: towards an evidence-based treatment guideline. Prog Retin Eye Res 73:100770. https://doi.org/10.1016/j.preteyeres.2019.07.003
Erikitola OC, Crosby-Nwaobi R, Lotery AJ, Sivaprasad S (2014) Photodynamic therapy for central serous chorioretinopathy. Eye 28(8):944–957. https://doi.org/10.1038/eye.2014.134
Kim KS, Lee WK, Lee SB (2014) Half-dose photodynamic therapy targeting the leakage point on the fluorescein angiography in acute central serous chorioretinopathy: a pilot study. Am J Ophthalmol 157(2):366–373. https://doi.org/10.1016/j.ajo.2013.10.013
Burumcek E, Mudun A, Karacorlu S, Arslan MO (1997) Laser photocoagulation for persistent central serous retinopathy. Ophthalmology 104(4):616–622. https://doi.org/10.1016/s0161-6420(97)30262-0
Leaver P, Williams C (1979) Argon laser photocoagulation in the treatment of central serous retinopathy. Br J Ophthalmol 63(10):674–677. https://doi.org/10.1136/bjo.63.10.674
Robertson DM, Ilstrup D (1983) Direct, indirect, and sham laser photocoagulation in the management of central serous chorioretinopathy. Am J Ophthalmol 95(4):457–466. https://doi.org/10.1016/0002-9394(83)90265-9
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. https://doi.org/10.1001/jama.2016.17216
Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211–2223. https://doi.org/10.1001/jama.2017.18152
Fleming AD, Goatman KA, Philip S, Williams GJ, Prescott GJ, Scotland GS, McNamee P, Leese GP, Wykes WN, Sharp PF, Olson JA, Scottish Diabetic Retinopathy Clinical Research N (2010) The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. Br J Ophthalmol 94(6):706–711. https://doi.org/10.1136/bjo.2008.149807
Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Garg S, Tobin KW Jr, Chaum E (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 16(1):216–226. https://doi.org/10.1016/j.media.2011.07.004
Keel S, Wu J, Lee PY, Scheetz J, He M (2019) Visualizing deep learning models for the detection of referable diabetic retinopathy and glaucoma. JAMA Ophthalmol 137(3):288–292. https://doi.org/10.1001/jamaophthalmol.2018.6035
Marin D, Gegundez-Arias ME, Ponte B, Alvarez F, Garrido J, Ortega C, Vasallo MJ, Bravo JM (2018) An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification. Med Biol Eng Comput 56(8):1379–1390. https://doi.org/10.1007/s11517-017-1771-2
Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM (2017) Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol 135(11):1170–1176. https://doi.org/10.1001/jamaophthalmol.2017.3782
Pead E, Megaw R, Cameron J, Fleming A, Dhillon B, Trucco E, MacGillivray T (2019) Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Surv Ophthalmol 64(4):498–511. https://doi.org/10.1016/j.survophthal.2019.02.003
Ahn JM, Kim S, Ahn KS, Cho SH, Lee KB, Kim US (2018) A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PLoS One 13(11):e0207982. https://doi.org/10.1371/journal.pone.0207982
Li Z, He Y, Keel S, Meng W, Chang RT, He M (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125(8):1199–1206. https://doi.org/10.1016/j.ophtha.2018.01.023
Fang L, Yang L, Li S, Rabbani H, Liu Z, Peng Q, Chen X (2017) Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning. J Biomed Opt 22(6):66014. https://doi.org/10.1117/1.JBO.22.6.066014
Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y (2018) Deep learning-based automated classification of multi-categorical abnormalities from optical coherence tomography images. Transl Vis Sci Technol 7(6):41. https://doi.org/10.1167/tvst.7.6.41
Liu YY, Ishikawa H, Chen M, Wollstein G, Duker JS, Fujimoto JG, Schuman JS, Rehg JM (2011) Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multiscale texture and shape features. Invest Ophthalmol Vis Sci 52(11):8316–8322. https://doi.org/10.1167/iovs.10-7012
Srinivasan PP, Kim LA, Mettu PS, Cousins SW, Comer GM, Izatt JA, Farsiu S (2014) Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express 5(10):3568–3577. https://doi.org/10.1364/BOE.5.003568
Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131 e1129. https://doi.org/10.1016/j.cell.2018.02.010
Khalid S, Akram MU, Hassan T, Jameel A, Khalil T (2018) Automated segmentation and quantification of drusen in fundus and optical coherence tomography images for detection of ARMD. J Digit Imaging 31(4):464–476. https://doi.org/10.1007/s10278-017-0038-7
Rabbani H, Allingham MJ, Mettu PS, Cousins SW, Farsiu S (2015) Fully automatic segmentation of fluorescein leakage in subjects with diabetic macular edema. Invest Ophthalmol Vis Sci 56(3):1482–1492. https://doi.org/10.1167/iovs.14-15457
Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197–207. https://doi.org/10.1016/j.media.2019.01.012
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention. pp 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
Cunefare D, Fang L, Cooper RF, Dubra A, Carroll J, Farsiu S (2017) Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks. Sci Rep 7(1):6620. https://doi.org/10.1038/s41598-017-07103-0
Fang LY, Cunefare D, Wang C, Guymer RH, Li ST, Farsiu S (2017) Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 8(5):2732–2744. https://doi.org/10.1364/Boe.8.002732
Lam C, Yu C, Huang L, Rubin D (2018) Retinal lesion detection with deep learning using image patches. Invest Ophthalmol Vis Sci 59(1):590–596. https://doi.org/10.1167/iovs.17-22721
Zhao YT, MacCormick IJC, Parry DG, Leach S, Beare NAV, Harding SP, Zheng YL (2015) Automated detection of leakage in fluorescein angiography images with application to malarial retinopathy. Sci Rep 5. https://doi.org/10.1038/srep10425
Phillips RP, Ross PG, Tyska M, Sharp PF, Forrester JV (1991) Detection and quantification of hyperfluorescent leakage by computer analysis of fundus fluorescein angiograms. Graefe's Archive for Clinical and Experimental Ophthalmology 229(4):329–335
Phillips RP, Spencer T, Ross PGB, Sharp PF, Forrester JV (1991) Quantification of diabetic maculopathy by digital imaging of the fundus. Eye (Lond) 5:130–137
Kernt M, Cheuteu R, Vounotrypidis E, Haritoglou C, Kampik A, Ulbig MW, Neubauer AS (2011) Focal and panretinal photocoagulation with a navigated laser (NAVILAS(R)). Acta Ophthalmol (Copenh) 89(8):e662–e664. https://doi.org/10.1111/j.1755-3768.2010.02017.x
Kozak I, Oster SF, Cortes MA, Dowell D, Hartmann K, Kim JS, Freeman WR (2011) Clinical evaluation and treatment accuracy in diabetic macular edema using navigated laser photocoagulator NAVILAS. Ophthalmology 118(6):1119–1124. https://doi.org/10.1016/j.ophtha.2010.10.007
Kernt M, Cheuteu R, Liegl RG, Seidensticker F, Cserhati S, Hirneiss C, Haritoglou C, Kampik A, Ulbig M, Neubauer AS (2012) Navigated focal retinal laser therapy using the NAVILAS(R) system for diabetic macula edema. Ophthalmologe 109(7):692–698. https://doi.org/10.1007/s00347-012-2559-2
Muller B, Tatsios J, Klonner J, Pilger D, Joussen AM (2018) Navigated laser photocoagulation in patients with non-resolving and chronic central serous chorioretinopathy. Graefes Arch Clin Exp Ophthalmol 256(9):1581–1588. https://doi.org/10.1007/s00417-018-4031-8