Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT

TL;DR

This paper introduces a novel 3D reconstruction and quantification framework for iris surfaces from AS-OCT images, improving angle-closure glaucoma detection over traditional 2D methods.

Contribution

It is the first to utilize 3D iris surface reconstruction from AS-OCT for glaucoma detection, combining segmentation, optimization, and feature extraction.

Findings

3D representation outperforms 2D in glaucoma detection accuracy

Proposed iris segmentation network with wavelet refinement improves initial shape estimation

Reconstruction method effectively captures morphological changes of the iris

Abstract

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.