  Optical coherence tomography angiography (OCTA) is a novel non-invasive
imaging technique that allows visualizations of vasculature and foveal
avascular zone (FAZ) across retinal layers. Clinical researches suggest that
the morphology and contour irregularity of FAZ are important biomarkers of
various ocular pathologies. Therefore, precise segmentation of FAZ has great
clinical interest. Also, there is no existing research reporting that FAZ
features can improve the performance of deep diagnostic classification
networks. In this paper, we propose a novel multi-level boundary shape and
distance aware joint learning framework, named BSDA-Net, for FAZ segmentation
and diagnostic classification from OCTA images. Two auxiliary branches, namely
boundary heatmap regression and signed distance map reconstruction branches,
are constructed in addition to the segmentation branch to improve the
segmentation performance, resulting in more accurate FAZ contours and fewer
outliers. Moreover, both low-level and high-level features from the
aforementioned three branches, including shape, size, boundary, and signed
directional distance map of FAZ, are fused hierarchically with features from
the diagnostic classifier. Through extensive experiments, the proposed BSDA-Net
is found to yield state-of-the-art segmentation and classification results on
the OCTA-500, OCTAGON, and FAZID datasets.
