3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

TL;DR

This paper introduces a novel 3D automatic segmentation method for retinal OCT volume data that enhances boundary detection accuracy by combining smoothing and differential filters, enabling effective segmentation of retinal layers in normal and abnormal eyes.

Contribution

The proposed method uniquely combines boundary surface enhancement with dynamic thresholding for improved 3D retinal layer segmentation in OCT data.

Findings

Accurately segmented seven retinal layer boundaries in normal and abnormal eyes.

Effective in processing large OCT datasets with high resolution.

Demonstrated robustness across multiple human OCT volumes.

Abstract

With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of data consisted of 97 OCT images with a resolution of 496 512; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.