10 Layer Segmentation and Analysis for Retina with Diseases
275
tested on a small-sized image dataset (15 eyes with retinal trauma and 15 normal
eyes). We are still collecting more data and will validate our method on a larger
dataset in the near future.
10.5 Conclusions
In this chapter, several methods for automatic layer segmentation and analysis of
diseased retinas are presented. Layer segmentation can serve as a pre-processing
step for both abnormal layer analysis and pathological region detection, while layer
disruption detection provides quantitative information of the change caused by retinal
pathology, which is of great value to diagnosis and progress tracking of the disease.
Though these methods achieve good performance, they are tailored for specific type
of pathology. More universal method that can deal with various types of diseases is
expected, and this remains an open and hot area in OCT image analysis.
References
1. M.K. Garvin, M.D. Abràmoff, X. Wu, S.R. Russell, T.L. Burns, M. Sonka, Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography
images. IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009)
2. K. Lee, Segmentations of the intraretinal surfaces, optic disc and retinal blood vessels in 3DOCT scans. Ph.D. dissertation, University of Iowa, 2009
3. S. Lu, C.Y. Cheung, J. Liu, J.H. Lim, C.K. Leung, T.Y. Wong, Automated layer segmentation of
optical coherence tomography images. IEEE Trans. Biomed. Eng. 57(10), 2605–2608 (2010)
4. A. Yazdanpanah, G. Hamarneh, B.R. Smith, M.V. Sarunic, Segmentation of intra-retinal layers
from optical coherence tomography images using an active contour approach. IEEE Trans.
Med. Imag. 30(2), 484–496 (2011)
5. Q. Song, J. Bai, M.K. Garvin, M. Sonka, J.M. Buatti, X. Wu, Optimal multiple surface segmentation with shape and context priors. IEEE Trans. Med. Imag. 32(2), 376–386 (2013)
6. Q. Yang, C.A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M.
Araie, A.S. Raza, D.C. Hood, K. Chan, Automated layer segmentation of macular OCT images
using dual-scale gradient information. Opt. Express 18(21), 293–307 (2010)
7. S.J. Chiu, X.T. Li, P. Nicholas, C.A. Toth, J.A. Izatt, S. Farsiu, Automatic segmentation of seven
retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express
18(18), 19413–19428 (2010)
8. J. Novosel, K.A. Vermeer, G. Thepass, H.G. Lemij, L.J. van Vliet, Loosely coupled level sets
for retinal layer segmentation in optical coherence tomography, in Proceedings of IEEE ISBI
(2013), pp 1010–1013
9. K.A. Vermeer, J. van der Schoot, H.G. Lemij, J.F. de Boer, Automated segmentation by pixel
classification of retinal layers in ophthalmic OCT images. Biomed. Opt. Express 2, 1743–1756
(2011)
10. R. Kafieh, H. Rabbani, M.D. Abramoff, M. Sonka, Intra-retinal layer segmentation of 3D optical
coherence tomography using coarse grained diffusion map. Med. Imag. Anal. 17, 907–928
(2013)
275
tested on a small-sized image dataset (15 eyes with retinal trauma and 15 normal
eyes). We are still collecting more data and will validate our method on a larger
dataset in the near future.
10.5 Conclusions
In this chapter, several methods for automatic layer segmentation and analysis of
diseased retinas are presented. Layer segmentation can serve as a pre-processing
step for both abnormal layer analysis and pathological region detection, while layer
disruption detection provides quantitative information of the change caused by retinal
pathology, which is of great value to diagnosis and progress tracking of the disease.
Though these methods achieve good performance, they are tailored for specific type
of pathology. More universal method that can deal with various types of diseases is
expected, and this remains an open and hot area in OCT image analysis.
References
1. M.K. Garvin, M.D. Abràmoff, X. Wu, S.R. Russell, T.L. Burns, M. Sonka, Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography
images. IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009)
2. K. Lee, Segmentations of the intraretinal surfaces, optic disc and retinal blood vessels in 3DOCT scans. Ph.D. dissertation, University of Iowa, 2009
3. S. Lu, C.Y. Cheung, J. Liu, J.H. Lim, C.K. Leung, T.Y. Wong, Automated layer segmentation of
optical coherence tomography images. IEEE Trans. Biomed. Eng. 57(10), 2605–2608 (2010)
4. A. Yazdanpanah, G. Hamarneh, B.R. Smith, M.V. Sarunic, Segmentation of intra-retinal layers
from optical coherence tomography images using an active contour approach. IEEE Trans.
Med. Imag. 30(2), 484–496 (2011)
5. Q. Song, J. Bai, M.K. Garvin, M. Sonka, J.M. Buatti, X. Wu, Optimal multiple surface segmentation with shape and context priors. IEEE Trans. Med. Imag. 32(2), 376–386 (2013)
6. Q. Yang, C.A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M.
Araie, A.S. Raza, D.C. Hood, K. Chan, Automated layer segmentation of macular OCT images
using dual-scale gradient information. Opt. Express 18(21), 293–307 (2010)
7. S.J. Chiu, X.T. Li, P. Nicholas, C.A. Toth, J.A. Izatt, S. Farsiu, Automatic segmentation of seven
retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express
18(18), 19413–19428 (2010)
8. J. Novosel, K.A. Vermeer, G. Thepass, H.G. Lemij, L.J. van Vliet, Loosely coupled level sets
for retinal layer segmentation in optical coherence tomography, in Proceedings of IEEE ISBI
(2013), pp 1010–1013
9. K.A. Vermeer, J. van der Schoot, H.G. Lemij, J.F. de Boer, Automated segmentation by pixel
classification of retinal layers in ophthalmic OCT images. Biomed. Opt. Express 2, 1743–1756
(2011)
10. R. Kafieh, H. Rabbani, M.D. Abramoff, M. Sonka, Intra-retinal layer segmentation of 3D optical
coherence tomography using coarse grained diffusion map. Med. Imag. Anal. 17, 907–928
(2013)