INTENSITY INHOMOGENEITY CORRECTION OF MACULAR OCT USING N3 AND RETINAL FLATSPACE

Abstract

As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.

Keywords: OCT; flat space; inhomogeneity correction; retina.

Fig. 1

A B-scan image with regression estimated boundaries overlaid in the native (top) and macular flat space (bottom).

Fig. 2

The initial gain field (bottom) is generated by dividing the flat space data (top) by the averaged template image (middle).

Fig. 3

Examples of synthetic gain fields constructing having a uniform gradient (top) and Gaussian shape (bottom). The true and estimated fields are on the left and right, respectively.

Fig. 4

Bar plot of the standard deviation of the intensities within each layer averaged across all subjects.

Fig. 5

Input images from two different subjects (left) before, (middle) after, and (right) the resulting gain field.