Automated segmentation of the macula by optical coherence tomography
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TLDR
This paper presents optical coherence tomography signal intensity variation based segmentation algorithms for retinal layer identification to reduce the calculation time required by layer identification algorithms.Abstract:
This paper presents optical coherence tomography (OCT) signal intensity variation based segmentation algorithms for retinal layer identification. Its main ambition is to reduce the calculation time required by layer identification algorithms. Two algorithms, one for the identification of the internal limiting membrane (ILM) and the other for retinal pigment epithelium (RPE) identification are implemented to evaluate structural features of the retina. Using a 830 nm spectral domain OCT device, this paper demonstrates a segmentation method for the study of healthy and diseased eyes.read more
Citations
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Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
TL;DR: This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming and results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert graders.
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Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma.
Jean-Claude Mwanza,Jonathan D. Oakley,Donald L. Budenz,Robert T. Chang,Robert T. Chang,O'Rese J. Knight,William J. Feuer +6 more
TL;DR: The Cirrus HD-OCT GCA algorithm can successfully segment macular GCIPL and measureGCIPL thickness with excellent intervisit reproducibility and longitudinal monitoring of GCIPl thickness may be possible with CirrusHD-O CT for assessing glaucoma progression.
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High-speed optical coherence tomography: basics and applications.
TL;DR: The fundamental limitations and advantages of time-domain and Fourier-domain interferometric detection methods are discussed, and new perspectives on functional imaging with the use of state-of-the-art high-speed OCT technology are demonstrated.
Journal ArticleDOI
Automated layer segmentation of macular OCT images using dual-scale gradient information
Qi Yang,Charles A. Reisman,Zhenguo Wang,Yasufumi Fukuma,Masanori Hangai,Nagahisa Yoshimura,Atsuo Tomidokoro,Makoto Araie,Ali S. Raza,Donald C. Hood,Kinpui Chan +10 more
TL;DR: A novel automated boundary segmentation algorithm is proposed for fast and reliable quantification of nine intra-retinal boundaries in optical coherence tomography (OCT) images, utilizing local and complementary global gradient information simultaneously simultaneously.
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Polarization sensitive optical coherence tomography in the human eye.
TL;DR: A variety of different applications of this technique are presented in ocular imaging that are ranging from the anterior to the posterior eye segment and the benefits of the method for imaging different diseases as, e.g., age related macula degeneration or glaucoma is demonstrated.
References
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Journal ArticleDOI
Macular Segmentation with Optical Coherence Tomography
Hiroshi Ishikawa,Daniel Stein,Gadi Wollstein,Gadi Wollstein,S. Beaton,S. Beaton,James G. Fujimoto,Joel S. Schuman,Joel S. Schuman +8 more
TL;DR: The newly developed macular segmentation algorithm described herein demonstrated its ability to quantify objectively the glaucomatous damage to RGCs and NFL and to discriminate between glaucatous and normal eyes.
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Optical coherence angiography
TL;DR: Noninvasive angiography is demonstrated for the in vivo human eye with high-speed spectral-domain optical coherence tomography and three-dimensional vasculature of ocular vessels has been visualized.
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Automated detection of retinal layer structures on optical coherence tomography images
TL;DR: Experimental results indicate that the proposed novel approach has good performance in speckle noise removal, enhancement and segmentation of the various cellular layers of the retina using the STRATUSOCTTM system.
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Retinal thickness measurements from optical coherence tomography using a Markov boundary model
TL;DR: Qualitatively, the boundaries detected by the automated system generally agreed extremely well with the true retinal structure for the vast majority of OCT images, and a robust, quantitatively accurate system can be expected to improve patient care.