Source: https://www.osapublishing.org/boe/abstract.cfm?uri=boe-2-9-2493
Timestamp: 2019-04-22 00:50:23+00:00

Document:
To provide a tool for quantifying the effects of retinitis pigmentosa (RP) seen on spectral domain optical coherence tomography images, an automated layer segmentation algorithm was developed. This algorithm, based on dual-gradient information and a shortest path search strategy, delineates the inner limiting membrane and three outer retinal boundaries in optical coherence tomography images from RP patients. In addition, an automated inner segment (IS)/outer segment (OS) contour detection method based on the segmentation results is proposed to quantify the locus of points at which the OS thickness goes to zero in a 3D volume scan. The segmentation algorithm and the IS/OS contour were validated with manual segmentation data. The segmentation and IS/OS contour results on repeated measures showed good within-day repeatability, while the results on data acquired on average 22.5 months afterward demonstrated a possible means to follow disease progression. In particular, the automatically generated IS/OS contour provided a possible objective structural marker for RP progression.
A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. Elzaizt, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1-2), 43–48 (1995).
M. Wojtkowski, T. Bajraszewski, P. Targowski, and A. Kowalczyk, “Real-time in vivo imaging by high-speed spectral optical coherence tomography,” Opt. Lett. 28(19), 1745–1747 (2003).
M. A. Apushkin, G. A. Fishman, K. R. Alexander, and M. Shahidi, “Retinal thickness and visual thresholds measured in patients with retinitis pigmentosa,” Retina 27(3), 349–357 (2007).
T. S. Aleman, A. V. Cideciyan, A. Sumaroka, E. A. Windsor, W. Herrera, D. A. White, S. Kaushal, A. Naidu, A. J. Roman, S. B. Schwartz, E. M. Stone, and S. G. Jacobson, “Retinal laminar architecture in human retinitis pigmentosa caused by Rhodopsin gene mutations,” Invest. Ophthalmol. Vis. Sci. 49(4), 1580–1590 (2008).
S. G. Jacobson, T. S. Aleman, A. Sumaroka, A. V. Cideciyan, A. J. Roman, E. A. Windsor, S. B. Schwartz, H. L. Rehm, and W. J. Kimberling, “Disease boundaries in the retina of patients with Usher syndrome caused by MYO7A gene mutations,” Invest. Ophthalmol. Vis. Sci. 50(4), 1886–1894 (2009).
S. G. Jacobson, A. V. Cideciyan, T. S. Aleman, A. Sumaroka, E. A. Windsor, S. B. Schwartz, E. Heon, and E. M. Stone, “Photoreceptor layer topography in children with leber congenital amaurosis caused by RPE65 mutations,” Invest. Ophthalmol. Vis. Sci. 49(10), 4573–4577 (2008).
D. C. Hood, C. E. Lin, M. A. Lazow, K. G. Locke, X. Zhang, and D. G. Birch, “Thickness of receptor and post-receptor retinal layers in patients with retinitis pigmentosa measured with frequency-domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 50(5), 2328–2336 (2009).
N. V. Rangaswamy, H. M. Patel, K. G. Locke, D. C. Hood, and D. G. Birch, “A comparison of visual field sensitivity to photoreceptor thickness in retinitis pigmentosa,” Invest. Ophthalmol. Vis. Sci. 51(8), 4213–4219 (2010).
D. C. Hood, M. A. Lazow, K. G. Locke, V. C. Greenstein, and D. G. Birch, “The transition zone between healthy and diseased retina in patients with retinitis pigmentosa,” Invest. Ophthalmol. Vis. Sci. 52(1), 101–108 (2011).
D. C. Hood, R. Ramachandran, K. Holopigian, M. A. Lazow, D. G. Birch, and V. C. Greenstein, “Method for deriving visual field boundaries from OCT scans of patients with retinitis pigmentosa,” Biomed. Opt. Express 2(5), 1106–1114 (2011).
Q. Yang, C. A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood, and K. Chan, “Automated layer segmentation of macular OCT images using dual-scale gradient information,” Opt. Express 18(20), 21293–21307 (2010).
D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
D. Cabrera Fernández, H. M. Salinas, and C. A. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express 13(25), 10200–10216 (2005).
M. Mujat, R. Chan, B. Cense, B. Park, C. Joo, T. Akkin, T. Chen, and J. de Boer, “Retinal nerve fiber layer thickness map determined from optical coherence tomography images,” Opt. Express 13(23), 9480–9491 (2005).
M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27(10), 1495–1505 (2008).
M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
T. Fabritius, S. Makita, M. Miura, R. Myllylä, and Y. Yasuno, “Automated segmentation of the macula by optical coherence tomography,” Opt. Express 17(18), 15659–15669 (2009).
A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17(26), 23719–23728 (2009).
V. Kajić, B. Považay, B. Hermann, B. Hofer, D. Marshall, P. L. Rosin, and W. Drexler, “Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis,” Opt. Express 18(14), 14730–14744 (2010).
S. Lu, C. Y.-I Cheung, J. Liu, J. H. Lim, C. K.-s. Leung, and T. Y. Wong, “Automated layer segmentation of optical coherence tomography images,” IEEE Trans. Biomed. Eng. 57(10), 2605–2608 (2010).
G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abramoff, and 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(6), 1321–1330 (2010).
S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
H. Zhu, D. P. Crabb, P. G. Schlottmann, T. Ho, and D. F. Garway-Heath, “FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography,” Opt. Express 18(24), 24595–24610 (2010).
A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Pearson Prentice Hall, 2004).
D. C. Hood, J. Cho, A. S. Raza, B. A. Dale, and W. Min, “Reliability of a computer-aided manual procedure for segmenting optical coherence tomography scans,” Optom. Vis. Sci. 88, 113–123 (2010).
Fig. 1 Illustration of four retinal boundaries on a scan from a patient with RP. From top to bottom: Boundary 1 ILM, Boundary 2 IS/OS, Boundary 3 OS/RPE, and Boundary 4 BM/Choroid (ILM, inner limiting membrane; IS, inner segment; OS, outer segment; RPE, retinal pigment epithelium; BM, Bruch’s membrane).
Fig. 2 Example of the BM/Choroid boundary detection improvement: (a) RP patient’s OCT image to be segmented, (b) segmentation results from the BM/Choroid search map with only gradient information, and (c) segmentation results from the enhanced BM/Choroid search map with additional gradient difference information.
Fig. 3 Illustration of the IS/OS boundary detection result on an expanded view of a portion of an enlarged OCT image: (a) IS/OS boundary located as the dark-to-bright edge, and (b) IS/OS boundary located as the high intensity line. Shown in the upper boundary is the IS/OS boundary and the lower is the OS/RPE boundary.
Fig. 5 Examples of both manual and automatic segmentation results from RP patients. The OCT images exhibit different degrees of OS thinning.
Fig. 6 Illustration of typical segmentation results and OS thickness map: (a) 6mm by 6mm (512 by 128 pixels) OS thickness map with the IS/OS contour shown (yellow boundary), (b) the segmentation image of the 30th B-scan image (512 by 128 pixels with an axial resolution of 3.5 µm/pixel), the yellow arrow indicates the point corresponding to the IS/OS contour; (c) the segmentation image of the 60th B-scan image (512 by 128 pixels with an axial resolution of 3.5 µm/pixel) where the yellow arrow indicates the OS disappearance point detected by IS/OS contour, (d) the expanded view of the OS disappearance area within the 30th B-scan image, and (e) the expanded view of the OS disappearance area within the 60th B-scan image.
Fig. 7 The 5mm by 5mm (107 by 107 pixels) thickness map and the IS/OS contour (yellow curves) results from right eyes of 6 patients for two repetitions of the two visits. The second visits were on average 22.5 months later from the first visit.
Fig. 8 The OS area difference versus the mean of the OS area from the two repetitions of the first visit in 11 eyes of 6 RP patients; the red dashed lines are the interval limits (average difference ± 1.96 standard deviation of the difference), the green dashed line represents the mean of the OS area difference and the yellow dashed line shows where there is no difference in OS area between the two repetitions.

References: V. 
 V. 
 V. 
 V. 
 V. 
 V. 

V. 
 V.