Method for differentiating retinal layers in oct image

Abstract

A method for differentiating retinal layers includes obtaining an optical coherence tomography retinal layer image of an eye to be examined and inputting it into a deep neural network, calculating probability P that each pixel is included in each layer from the inputted layer image, calculating probability P1(x,y) that a pixel is in or above (k−1)-th layer and probability P2(x,y) that the pixel is in or below k-th layer from probabilities P(x,y) that the pixel is included in each layer, calculating probability F(x,y) that the pixel becomes a boundary line between the (k−1)-th layer and the k-th layer from P1() and P2(), obtaining a node cost C(x,y) of the pixel by multiplying F(x,y) with an image brightness change ΔI at the pixel, and determining the boundary line between the (k−1)-th and k-th layers by connecting coordinates (x,y) of the pixels where C(x,y) calculated for each position x is the maximum.