Automated Choroidal Neovascularization Detection for Time Series SD-OCT Images

Choroidal neovascularization (CNV), caused by new blood vessels in the choroid growing through the Bruch’s membrane, is an important manifestation of terminal age-related macular degeneration (AMD). Automated CNV detection in three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) images is still a huge challenge. This paper presents an automated CNV detection method based on object tracking strategy for time series SD-OCT volumetric images. In our proposed scheme, experts only need to manually calibrate CNV lesion area for the first moment of each patient, and then the CNV of the following moments will be automatically detected. In order to fully represent space consistency of CNV, a 3D-histogram of oriented gradient (3D-HOG) feature is constructed for the generation of random forest model. Finally, the similarity between training and testing samples is measured for model updating. The experiments on 258 SD-OCT cubes from 12 eyes in 12 patients with CNV demonstrate that our results have a high correlation with the manual segmentations. The average of correlation coefficients and overlap ratio for CNV projection area are 0.907 and 83.96%, respectively.