Application of Deep Learning for Advanced Detection and Quantification of Drusen in Nonexudative AMD From Retinal...

To propose a novel deep learning-based methodology for drusen detection and quantification in early age-related macular degeneration (AMD) using retinal multispectral images. The retinal multispectral images highlight features in several nonoverlapping spectral bands that the deep learning models leverage for automatic drusen detection and quantification in dry AMD.

The proposed novel methodology comprises quality assessment of retinal images, region of interest extraction, drusen segmentation, and drusen quantification stages. Different deep learning models (such as UNet++ convolutional neural network with EfficientNetV2 encoder) have been implemented for these stages. A total of 170 drusen and 150 nondrusen retinal images (single eye) were split into four training and validation data sets to analyze the performance of a deep learning model for drusen segmentation.

The proposed methodology achieved an average score, recall, and precision of 0.691, 0.668, and 0.776, respectively, across all four validation sets. This work also analyzed the performance of the proposed deep learning model for discriminating drusen and drusen-like lesions, achieving a pixel-wise segmentation accuracy of 99.998%. The number and the diameter of the detected drusen were also computed. A Dice score distribution for drusen segmentation with different numbers and sizes of drusen per eye is also shown.

This work demonstrates that deep learning models applied to retinal multispectral images can provide accurate and clinically significant drusen segmentation and quantification, thereby facilitating early detection, longitudinal monitoring, and reduction of the risk of vision loss from AMD.

Deep learning-assisted detection of drusen from multispectral retinal images will refine and improve clinical diagnosis of early nonexudative age-related macular degeneration.