Simultaneous Segmentation of Geographic Atrophy in Longitudinally Acquired Fundus Autofluorescence Images

Accurately measuring geographic atrophy (GA) progression in clinical trials is challenging, owing to its slow and variable nature. This study develops deep learning algorithms that can measure GA growth rates with improved accuracy and robustness, by performing automated annotation of pre-existing versus newly expanding GA regions, based on simultaneous grading of fundus autofluorescence (FAF) image pairs.

Retrospective analysis of data acquired in 2 independent prospective clinical trials (i.e., 830 images in AREDS2 [Age-Related Eye Disease Study 2] and 273 images in METformin for the MINimization of Geographic Atrophy Progression [METforMIN]).

One hundred seventy-four AREDS2 and 44 METforMIN participants.

A standard DeepLabV3+ (DL) segmentation model that automatically segments GA from a single FAF image (DeepLabV3+ _STATIC [DL_STATIC]) was enhanced to input a longitudinally acquired, spatially aligned FAF image pair and to simultaneously segment regions corresponding to existing and expanding GA pixels (DeepLabV3+_JOINT [DL_JOINT]). This model was further modified to ensure longitudinal consistency of existing GA pixels (DL_JOINT+SCL).

Image pixel overlap (using precision, recall, and Dice), GA area, and GA growth rates were measured with manual contours as the gold standard. The study also quantified the effect of longitudinal alignment of images to GA growth measurements.

When compared to manual expert annotations, the DL_JOINT+SCL model exhibited the highest performance, with precision (mean ± standard deviation) = 0.86 ± 0.13, recall = 0.92 ± 0.11, Dice = 0.89 ± 0.11, highest GA area correlation (R² = 0.97), and highest growth rate correlation (R² = 0.82). This shows the utility of simultaneously segmenting images when computing growth. Comparatively, the DL_STATIC model exhibited precision = 0.84 ± 0.13, recall = 0.93 ± 0.15, Dice = 0.87 ± 0.13, area correlation R² = 0.94, and growth rate correlation R² = 0.75. Spatial alignment via image registration was a key step that enabled the algorithm implementation; this yielded accuracy improvements when measuring GA growth by mapping regions to a common spatial coordinate system.

The simultaneous segmentation of longitudinal FAF images yielded improvements in accuracy and robustness by maintaining the longitudinal consistency of measurements. Such improvements are critical to the translation of automatic algorithms and minimizing measurement variability to effectively assess clinical trial outcomes in GA.

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