A 3D Segmentation Method for Retinal Optical Coherence Tomography Volume Data
This addresses the need for 3D segmentation in medical imaging for processing larger OCT datasets, but it appears incremental as it builds on existing segmentation approaches.
The paper tackled the problem of segmenting retinal optical coherence tomography volume data by developing a new 3D segmentation method that uses pixel intensity, boundary position, and intensity changes to generate enhanced data and smoothed boundary surfaces. The result is an efficient, accurate, and robust method, though no concrete numbers are provided.
With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We present a new 3D segmentation method for retinal OCT volume data, which generates an enhanced volume data by using pixel intensity, boundary position information, intensity changes on both sides of the border simultaneously, and preliminary discrete boundary points are found from all A-Scans and then the smoothed boundary surface can be obtained after removing a small quantity of error points. Our experiments show that this method is efficient, accurate and robust.