Leveraging side-view feature guidance for robust vascular volume analysis in OCTA

Abstract

Ocular diseases require accurate diagnosis and continuous monitoring to improve patient outcomes. Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging modality that provides detailed visualization of ocular vascular structures. However, existing methods face challenges, including loss of slice-direction information due to projection-based down-sampling and incomplete vascular representation from single-view images. To address these limitations, we propose a side-view feature-guided network (SVF-Net) for OCTA volume analysis. The model integrates overlay-based feature extraction to preserve fine-grained details across slices and employs a side-view attention mechanism to capture diverse features and reduce information loss. Experiments on the OCTA-500 and ROSSA datasets demonstrate that our method outperforms significant benchmarks, achieving superior segmentation accuracy and effectively delineating complex vascular structures.