بهبود استخراج لایه‌ی Retinal Pigment Epithelium (RPE) در تصاویر Optical Coherence Tomography (OCT) با استفاده از روش قطعه‌ای برنامه‌نویسی پویا در بیماران دچار جداشدگی اپی‌تلیال رنگدانه

نوع مقاله : مقاله های پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه بیوالکتریک، دانشکده‌ی فن‌آوری‌های نوین علوم پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران

2 استادیار، گروه بیوالکتریک، دانشکده‌ی فن‌آوری‌های نوین علوم پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران

3 دانشیار، گروه چشم پزشکی، دانشکده‌ی پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران

چکیده

مقدمه: تخریب ماکولایی وابسته به سن (Age-related macular degeneration یا AMD) یکی از اختلالات به وجود آمده در شبکیه است که موجب اختلال بینایی مرکزی می‌گردد. جهت تشخیص این بیماری از تصاویر Optical coherence tomography (OCT) استفاده می‌شود و با توجه به تغییرات به وجود آمده‌ی ناشی از بیماری به صورت ایجاد بالازدگی‌هایی در لایه‌ی Retinal pigment epithelium (RPE) شبکیه، عارضه مورد تشخیص قرار می‌گیرد.روش‌ها: در روش پیشنهادی، نقطه‌ی ابتدایی لایه‌ی RPE در تعداد محدودی از اسلایدهای OCT توسط کاربر علامت‌گذاری می‌شود تا احتمال تشخیص اشتباه لایه‌های دیگر مانند Retinal nerve fiber layer (RNFL) از بین برود. سپس، الگوریتم مبتنی بر گراف بر مبنای برنامه‌نویسی پویا در قطعات کم عرض به تصویر اعمال شده، الگوریتمی برای حفظ پیوستگی قطعات استفاده شد تا در نهایت، مکان لایه‌ی RPE تخمین زده شود. با الگوریتمی مشابه، لایه‌ی Bruch نیز در هر اسکن مکان‌یابی شد و با تخمین فاصله‌ی دو لایه، بالازدگی‌های Persistent epithelial defect (PED) شناسایی گردید.یافته‌ها: روش پیشنهادی بر روی سه دیتاست با تعداد 35، 15 و 10 بیمار بررسی شد. در مقایسه با روش مبتنی بر گراف در دیتاست اول، دوم و سوم میزان خطای بدون علامت در لایه‌ی RPE به ترتیب از 3392/4 به 7827/2، از 3340/3 به 1623/2 و از 4842/6 به 3924/2 پیکسل و در لایه‌ی Bruch از 7576/5 به 8473/4، از 3353/4 به 6023/2 و از 67/6 به 5446/2 پیکسل بهبود یافت.نتیجه‌گیری: روش پیشنهادی در تصاویر سالم و دارای PED قابل اعتبار است و می‌تواند در امر تشخیص بیماری AMD مؤثر واقع شود.

کلیدواژه‌ها


عنوان مقاله [English]

The Improvement of Extraction Retinal Pigment Epithelium (RPE) Layer in Optical Coherence Tomography (OCT) Images by Using Piecewise Dynamic Programming Method in Patients with Persistent Epithelial Defect (PED)

نویسندگان [English]

  • Alireza Haghani 1
  • Rahele Kafieh 2
  • Mohammadreza Akhlaghi 3
1 MSc Student, Department of Bioelectric, School of Advanced Technologies in Medical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
2 Assistant Professor, Department of Bioelectric, School of Advanced Technologies in Medical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
3 Professor, Department of Ophthalmology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
چکیده [English]

Background: Age-related macular degeneration (AMD) is one of the disorders in the retina that causes central vision disorders. Optical coherence tomography (OCT) images are used to diagnose this disease, and given the changes in the disease caused by elevations in the retinal pigment epithelium (RPE) layer of the retina, the complication is diagnosed.Methods: In the proposed method, the starting point of the RPE layer was labeled by the user on a limited number of OCT slides to eliminate the possibility of mistaking other layers such as the retinal nerve fiber layer (RNFL). The graph-based algorithm was then applied to the image in low width parts; an algorithm was used to maintain the continuity of the parts to eventually estimate the location of the RPE layer. With a similar algorithm, the bruch layer was also located at each scan, and by estimating the distance of the two layers, PED elevations were identified.Findings: The proposed method was evaluated on three datasets with 35, 15, and 10 patients. Compared to the graph-based method in the first, second, and third datasets, respectively, the unsigned error in the RPE layer improved from 4.3392 to 2.7827, 3.3340 to 2.1623, and 6.4842 to 2.3924 pixels, and the bruch layer improved from 5.7576 to 4.8473, 4.3353 to 2.6023, and 6.67 to 2.5446 pixels, respectively.Conclusion: The proposed method is valid in PED images, and can be effective in diagnosing AMD.

کلیدواژه‌ها [English]

  • Retinal pigment epithelium
  • Optical coherence tomography
  • Age-related macular degeneration
  • Retinal pigment epithelial detachment
  1. Wintergerst MWM, Schultz T, Birtel J, Schuster AK, Pfeiffer N, Schmitz-Valckenberg S, et al. Algorithms for the automated analysis of age-related macular degeneration biomarkers on optical coherence tomography: A systematic review. Transl Vis Sci Technol 2017; 6(4): 10.
  2. Chen Z, Li D, Shen H, Mo H, Zeng Z, Wei H. Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration. Opt Laser Technol 2020; 122: 105830.
  3. Hamwood J, Alonso-Caneiro D, Read SA, Vincent SJ, Collins MJ. Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. Biomed Opt Express 2018; 9(7): 3049-66.
  4. Gorgi Zadeh S, Wintergerst MWM, Schultz T. Uncertainty-guided semi-automated editing of CNN-based retinal layer segmentations in optical coherence tomography. Proceedings of 10th the Annual Eurographics Workshop on Visual Computing for Biology and Medicine; 2018 Sep 20-21; Granada, Spain.
  5. Gorgi Zadeh S, Wintergerst MWM, Schultz T. Intelligent interaction and uncertainty visualization for efficient drusen and retinal layer segmentation in Optical Coherence Tomography. Comput Graph 2019; 83: 51-61.
  6. Saha S, Nassisi M, Wang M, Lindenberg S, kanagasingam Y, Sadda S, et al. Automated detection and classification of early AMD biomarkers using deep learning. Sci Rep 2019; 9(1): 10990.
  7. Farsiu S, Chiu S, Izatt J, Toth C. Fast detection and segmentation of drusen in retinal optical coherence tomography images - art. No. 68440D. Proceedings of SPIE - The International Society for Optical Engineering 6844. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 2008; 6844.
  8. Jain N, Farsiu S, Khanifar AA, Bearelly S, Smith RT, Izatt JA, et al. Quantitative comparison of drusen segmented on SD-OCT versus drusen delineated on color fundus photographs. Invest Ophthalmol Vis Sci 2010; 51(10): 4875-83.
  9. Chen Q, de SL, Leng T, Zheng L, Kutzscher L, Rubin DL. Semi-automatic geographic atrophy segmentation for SD-OCT images. Biomed Opt Express 2013; 4(12): 2729-50.
  10. Hu Z, Medioni GG, Hernandez M, Hariri A, Wu X, Sadda SR. Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images. Invest Ophthalmol Vis Sci 2013; 54(13): 8375-83.
  11. Chiu SJ, Izatt JA, O'Connell RV, Winter KP, Toth CA, Farsiu S. Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest Ophthalmol Vis Sci 2012; 53(1): 53-61.
  12. Ahlers C, Simader C, Geitzenauer W, Stock G, Stetson P, Dastmalchi S, et al. Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography. Br J Ophthalmol 2008; 92(2): 197-203.
  13. Penha FM, Rosenfeld PJ, Gregori G, Falcao M, Yehoshua Z, Wang F, et al. Quantitative imaging of retinal pigment epithelial detachments using spectral-domain optical coherence tomography. Am J Ophthalmol 2012; 153(3): 515-23.
  14. Kafieh R, Rabbani H, Abramoff MD, Sonka M. Curvature correction of retinal OCTs using graph-based geometry detection. Phys Med Biol 2013; 58(9): 2925-38.
  15. Ho J, Adhi M, Baumal C, Liu J, Fujimoto JG, Duker JS, et al. Agreement and reproducibility of retinal pigment epithelial detachment volumetric measurements through optical coherence tomography. Retina 2015; 35(3): 467-72.
  16. Li K, Wu X, Chen DZ, Sonka M. Optimal surface segmentation in volumetric images--a graph-theoretic approach. IEEE Trans Pattern Anal Mach Intell 2006; 28(1): 119-34.
  17. Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 1973; SMC-3(6): 610-21.
  18. Xiayu X, Kyungmoo L, Li Z, Sonka M, Abramoff MD. Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data. IEEE Trans Med Imaging 2015; 34(7): 1616-23.
  19. Dolejsi M, Abràmoff MD, Sonka M, Kybic J. Semi-automated segmentation of symptomatic exudate-associated derangements (SEADs) in 3D OCT using layer segmentation. Analysis of Biomedical 2010: 147.32.84.2.
  20. Liu YY, Ishikawa H, Chen M, Wollstein G, Duker JS, Fujimoto JG, et al. Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multiscale texture and shape features. Invest Ophthalmol Vis Sci 2011; 52(11): 8316-22.
  21. Sun Z, Chen H, Shi F, Wang L, Zhu W, Xiang D, et al. An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images. Sci Rep 2016; 6: 21739.
  22. Ding W, Young M, Bourgault S, Lee S, Albiani DA, Kirker AW, et al. Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013: 7388-91.
  23. Blair L, Morbey ML. Arts and Learning Research, 1992-1993. The Journal of the Arts and Learning Special Interest Group of the American Educational Research Association (San Francisco, California, April 1992; Atlanta, Georgia, April 1993). Arts and Learning Research. 1993; 10(1): 1992-3.
  24. Serrano-Aguilar P, Abreu R, Anton-Canalis L, Guerra-Artal C, Ramallo-Farina Y, Gomez-Ulla F, et al. Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography. Br J Ophthalmol 2012; 96(4): 503-7.
  25. Lee SY, Stetson PF, Ruiz-Garcia H, Heussen FM, Sadda SR. Automated characterization of pigment epithelial detachment by optical coherence tomography. Invest Ophthalmol Vis Sci 2012; 53(1): 164-70.
  26. Hartigan JA. Statistical theory in clustering. J Classif 1985; 2(1): 63-76.
  27. Kanagasingam Y, Bhuiyan A, Abramoff MD, Smith RT, Goldschmidt L, Wong TY. Progress on retinal image analysis for age related macular degeneration. Prog Retin Eye Res 2014; 38: 20-42.
  28. Wilkins GR, Houghton OM, Oldenburg AL. Automated segmentation of intraretinal cystoid fluid in optical coherence tomography. IEEE Trans Biomed Eng 2012; 59(4): 1109-14.
  29. Niemeijer M, Lee K, Chen X, Zhang L, Sonka M, Abramoff MD. Automated Estimation of Fluid Volume in 3D OCT Scans of Patients with CNV Due to AMD. Invest Ophthalmol Vis Sci 2012; 53(14): 4074.
  30. Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS, et al. Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration. Ophthalmol Retina 2018; 2(1): 24-30.
  31. Farsiu S, Chiu SJ, O'Connell RV, Folgar FA, Yuan E, Izatt JA, et al. Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 2014; 121(1): 162-72.
  32. Bellman R, Dreyfus SE. Applied Dynamic Programming. Princeton, NJ: Princeton University Press; 1962.