Age-related macular degeneration (AMD) is an eye condition that is considered to be one of the leading causes of blindness among people over 50. Recent studies suggest that the mechanical properties in retina layers are affected during the early onset of disease. Therefore, it is necessary to identify such changes in the individual layers of the retina so as to provide useful information for disease diagnosis. In this study, we propose using an acoustic radiation force optical coherence elastography (ARF-OCE) system to dynamically excite the porcine retina and detect the vibrational displacement with phase resolved Doppler optical coherence tomography. Due to the vibrational mechanism of the tissue response, the image quality is compromised during elastogram acquisition. In order to properly analyze the images, all signals, including the trigger and control signals for excitation, as well as detection and scanning signals, are synchronized within the OCE software and are kept consistent between frames, making it possible for easy phase unwrapping and elasticity analysis. In addition, a combination of segmentation algorithms is used to accommodate the compromised image quality. An automatic 3D segmentation method has been developed to isolate and measure the relative elasticity of every individual retinal layer. Two different segmentation schemes based on random walker and dynamic programming are implemented. The algorithm has been validated using a 3D region of the porcine retina, where individual layers have been isolated and analyzed using statistical methods. The errors compared to manual segmentation will be calculated.
Youmin He, Yueqiao Qu, Yi Zhang, Teng Ma, Jiang Zhu, Yusi Miao, Mark Humayun, Qifa Zhou, and Zhongping Chen, "Quantitative analysis of retina layer elasticity based on automatic 3D segmentation (Conference Presentation)," Proc. SPIE 10067, Optical Elastography and Tissue Biomechanics IV, 100670C (Presented at SPIE BiOS: January 29, 2017; Published: 24 April 2017); https://doi.org/10.1117/12.2251085.5380018824001.
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