Fully-automatic end-to-end approaches for 3D drusen segmentation in Optical Coherence Tomography images

Abstract

[Abstract]: Drusen, small lipid deposits located below the retina, are early biomarkers of age-related macular degeneration (AMD), a condition that leads to visual impairment worldwide, especially among the elderly. The presence of AMD is linked with Alzheimer’s Disease (AD) and dense deposit disease (DDD), emphasizing the critical need for early and accurate detection of drusen in retinal tissues. Optical Coherence Tomography (OCT), with its non-invasive and high-resolution imaging capabilities, stands as a pivotal tool for early AMD diagnosis through the identification of drusen. However, the reliance on manual segmentation of drusen in 3D OCT images introduces significant challenges: it is not only time-consuming but also subject to inter-observer variability, severely constraining its efficacy for widespread screening applications. These limitations underscore the critical need for an automated solution that can improve diagnostic workflows, ensure consistency across interpretations, and facilitate the processing of large datasets with improved accuracy and efficiency. In response, we propose a new deep learning-based, fully-automatic end-to-end approach for the segmentation of drusen in 3D OCT volumes. Complementary, a pivotal aspect of our research involves the first detailed comparative analysis between 2D and 3D end-to-end approaches. This comparison is crucial for understanding the impact of dimensional spatial information on the accuracy of drusen segmentation within OCT scans. The findings demonstrate that the 3D approach, by leveraging the depth and complexity of spatial data available in 3D OCT volumes, markedly surpasses the 2D approach. This superior performance underlines the importance of 3D spatial information in enhancing diagnostic precision. Through automating the segmentation process, our proposal not only makes AMD screening more efficient and precise but also significantly advances the diagnosis of retinal diseases, potentially enriching our understanding of systemic conditions connected through retinal biomarkers.