  Precise characterization and analysis of iris shape from Anterior Segment OCT
(AS-OCT) are of great importance in facilitating diagnosis of
angle-closure-related diseases. Existing methods focus solely on analyzing
structural properties identified from the 2D slice, while accurate
characterization of morphological changes of iris shape in 3D AS-OCT may be
able to reveal in addition the risk of disease progression. In this paper, we
propose a novel framework for reconstruction and quantification of 3D iris
surface from AS-OCT imagery. We consider it to be the first work to detect
angle-closure glaucoma by means of 3D representation. An iris segmentation
network with wavelet refinement block (WRB) is first proposed to generate the
initial shape of the iris from single AS-OCT slice. The 3D iris surface is then
reconstructed using a guided optimization method with Poisson-disk sampling.
Finally, a set of surface-based features are extracted, which are used in
detecting of angle-closure glaucoma. Experimental results demonstrate that our
method is highly effective in iris segmentation and surface reconstruction.
Moreover, we show that 3D-based representation achieves better performance in
angle-closure glaucoma detection than does 2D-based feature.
