Exploring Temporal Constraints for Unsupervised Iris Motion Tracking in AS-OCT Videos
IEEE International Conference on Acoustics, Speech, and Signal Processing（2025）
Abstract
Iris motion tracking is critical for discriminating the iris stiffness and developmental stage of primary angle-closure disease (PACD). Anterior segment optical coherence tomography (AS-OCT) video is a highly efficient approach to observe the morphological determinant in iris motion. However, the iris exhibits inconsistent elastic changes during movement, accompanied by changes in local features after long-term frames. Currently, iris tracking methods have not yet been studied in AS-OCT videos. In this paper, we propose a Temporal Constraint-based Tracking Morph (TCTMorph) for estimating iris trajectory in long-term AS-OCT videos. We first estimate the deformation fields between three interrelated frames by a multi-frame diffeomorphic registration network. Then, we estimate iris trajectory from these results in long-term AS-OCT video sequences by leveraging temporal constraints among the consecutive flows. Our experiments on multi-center AS-OCT glaucoma datasets demonstrate that our method outperforms conventional motion tracking methods for long-term iris trajectory tracking.
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Key words
Motion Tracking,Deep Learning,AS-OCT,Primary Angle-Closure Glaucoma
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