Long-term stabilized iris tracking with unsupervised constraints on dynamic AS-OCT

Abstract

Primary angle-closure glaucoma (PACG) is responsible for half of all glaucoma-related blindness worldwide. The devastating disease is often clinically silent before causing irreversible visual damage. Glaucomatous optic neuropathy is the major diagnostic criterion for glaucoma. Patients with severe PACG have been clinically found to have significantly lower pupillary reflex velocity and higher iris rigidity. Anterior segment optical coherence tomography (AS-OCT) enables dynamic visualization of the ocular iris anatomy which cannot otherwise be acquired by other imaging modalities. However, automatic quantification of dynamic iris motion on AS-OCT has not yet been implemented. The main challenges lie in the frequent jitter of high-resolution optical imaging, irregular temporal variations of elastic features, and relatively scarce datasets. In this paper, we propose an unsupervised constraint-based jitter refinement tracking (CJRTrack) framework for long-term AS-OCT video tracking. CJRTrack primarily consists of three modules: it first extracts a set of key regions from low-resolution images using an off-the-shelf point tracking algorithm. Given the initialized frames and points, an unsupervised multi-frame differentiable registration network estimates the localized deformation field patch for corresponding high-resolution images. It then refines these predictions using a temporal topology constraint-based module, which explicitly ensures overall trajectory stabilization and tracking. Multi-scale evaluations on two independent AS-OCT datasets demonstrate that CJRTrack significantly outperforms existing tracking models in both accuracy and stability. The clinical adaptivity of the model is further assessed on a glaucoma dataset containing 543 diseased eyes. Jitter-corrected quantification is extracted and used to classify neuropathic damage in primary angle closure patients.