Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics

TL;DR

This paper introduces a new open dataset of OCTA images with manual segmentations, compares various segmentation methods including deep learning, and emphasizes the importance of preserving vascular network structure for clinical metrics.

Contribution

It provides the first open OCTA dataset with ground truth, benchmarks segmentation methods, and proposes new metrics for network structure evaluation.

Findings

Deep learning models outperform traditional methods in segmentation accuracy.

Optimal oriented flux is the best handcrafted filter among tested methods.

Segmentation method choice can cause up to 25% variation in vascular metrics.

Abstract

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality for the visualisation of microvasculature in vivo that has encountered broad adoption in retinal research. OCTA potential in the assessment of pathological conditions and the reproducibility of studies relies on the quality of the image analysis. However, automated segmentation of parafoveal OCTA images is still an open problem. In this study, we generate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Furthermore, we establish a standard for OCTA image segmentation by surveying a broad range of state-of-the-art vessel enhancement and binarisation procedures. We provide the most comprehensive comparison of these methods under a unified framework to date. Our results show that, for the set of images considered, deep learning architectures (U-Net and CS-Net) achieve the best performance. For applications where manually segmented data is not available to retrain these approaches, our findings suggest that optimal oriented flux is the best handcrafted filter from those considered. Furthermore, we report on the importance of preserving network structure in the segmentation to enable deep vascular phenotyping. We introduce new metrics for network structure evaluation in segmented angiograms. Our results demonstrate that segmentation methods with equal Dice score perform very differently in terms of network structure preservation. Moreover, we compare the error in the computation of clinically relevant vascular network metrics (e.g. foveal avascular zone area and vessel density) across segmentation methods. Our results show up to 25% differences in vessel density accuracy depending on the segmentation method employed. These findings should be taken into account when comparing the results of clinical studies and performing meta-analyses.