Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy

Abstract

Stargardt disease and age-related macular degeneration are the leading causes of blindness in the juvenile and geriatric populations, respectively. The formation of atrophic regions of the macula is a hallmark of the end-stages of both diseases. The progression of these diseases is tracked using various imaging modalities, two of the most common being fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT). This study seeks to investigate the use of longitudinal FAF and SD-OCT imaging (month 0, month 6, month 12, and month 18) data for the predictive modelling of future atrophy in Stargardt and geographic atrophy. To achieve such an objective, we develop a set of novel deep convolutional neural networks enhanced with recurrent network units for longitudinal prediction and concurrent learning of ensemble network units (termed ReConNet) which take advantage of improved retinal layer features beyond the mean intensity features. Using FAF images, the neural network presented in this paper achieved mean (± standard deviation, SD) and median Dice coefficients of 0.895 (± 0.086) and 0.922 for Stargardt atrophy, and 0.864 (± 0.113) and 0.893 for geographic atrophy. Using SD-OCT images for Stargardt atrophy, the neural network achieved mean and median Dice coefficients of 0.882 (± 0.101) and 0.906, respectively. When predicting only the interval growth of the atrophic lesions with FAF images, mean (± SD) and median Dice coefficients of 0.557 (± 0.094) and 0.559 were achieved for Stargardt atrophy, and 0.612 (± 0.089) and 0.601 for geographic atrophy. The prediction performance in OCT images is comparably good to that using FAF which opens a new, more efficient, and practical door in the assessment of atrophy progression for clinical trials and retina clinics, beyond widely used FAF. These results are highly encouraging for a high-performance interval growth prediction when more frequent or longer-term longitudinal data are available in our clinics. This is a pressing task for our next step in ongoing research.

Figure 1.

Schematic of the ReConNet neural network architecture.

Figure 2.

Ensemble neural network structure. The OCT feature maps include traditional mean intensity maps, and additional minimum intensity, maximum intensity, median intensity, standard deviation, skewness, kurtosis, gray level entropy, and thickness of the ellipsoid zone.

Figure 3.

Schematic of the prediction algorithm, where i in the subscript indicates the initial prediction results from ReconNet1 and f in the subscript indicates the final prediction results from ReConNet2.

Figure 4.

Example results of ReConNet. Input FAF images and labels, initial prediction, final prediction, and ground truth comparison for ReConNet with Stargardt atrophy (Top) and geographic atrophy (Bottom).

Figure 5.

Example results after ReConNet-Ensemble. Input OCT feature maps, initial prediction, final prediction, and ground truth comparison for ensemble ReConNet with Stargardt atrophy. Input labels are not pictured.

Figure 6.

Example results after ReConNet1-Interval. (Top) Input modified FAF images, labels, interval growth prediction, and ground truth comparison with Stargardt atrophy. (Middle and Bottom) Input modified FAF images, labels, interval growth prediction, and ground truth comparison with geographic atrophy. 18-Month FAF images are shown for reference.