Automated Segmentation of Esophagus Layers from OCT Images Using Fast Marching Method

Thickness of the esophagus is an important diagnostic marker for many esophagus diseases. While labeling boundaries by manual to compute each layer's average thickness is time-consuming and subjective. In this paper, we present a new fully automatic algorithm which includes Fast Marching Method (FMM) and Fourth-Order Runge-Kutta method (RK4) to identify five esophagus layers on optical coherence tomography (OCT) images. FMM is used to calculate the weighted geodesic distance. In particular, the velocity function involved in this method combines vertical gradient, horizontal gradient and curvature so that it not only can divide flat borders but also irregular borders. RK4 is used to find the shortest path which is the boundary to be segmented. The experimental comparison between automatic and manual is performed on 400 healthy guinea pig esophagus OCT images and the mean absolute error thickness difference between them is less than 6 pixels while the value can reach to 9.41 pixels at most between two observers.