Real-time corneal nerve image stitching enhances wider view during exams
CRISP: Corneal Confocal Microscopy Real-Time Image Stitching Pipeline
Computer Vision and Pattern Recognition
Summary
Seeing the tiny nerves in the eye's cornea helps doctors understand nerve health. But existing cameras only capture small parts at a time, so it’s hard to get a full picture. The authors created CRISP, a new open-source tool that stitches these small images together in real-time during exams. This gives a bigger, clearer view without special equipment or complex steps. CRISP could make wide-area corneal nerve imaging easier to use in everyday medical check-ups.
What this means in practice
- •For ophthalmic clinicians: Enable real-time wide-area imaging of corneal nerves during standard eye exams to improve nerve health assessment.
- •For medical device engineers: Incorporate open-source real-time image stitching into corneal microscopy devices without adding hardware or changing exam protocols.
Authors
Qincheng Qiao, Puli Zhang, Jian Zhou, Xinguo Hou
Abstract
Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for in vivo, real-time, non-invasive observation of the SNP. However, mainstream CCM devices offer a limited field of view per frame, whereas the SNP is spatially non-uniform; discrete image sampling is therefore sensitive to sampling location and frame selection, which limits the reproducibility and clinical adoption of CCM as a quantitative assessment tool. Wide-field stitching can reconstruct larger SNP mosaics by integrating sequentially acquired CCM images, but existing methods largely rely on offline post-processing, additional hardware, or specific acquisition protocols, and lack open-source real-time solutions for conventional CCM video streams. This paper presents CRISP (Corneal confocal microscopy Real-time Image Stitching Pipeline), an open-source real-time SNP wide-field stitching framework for conventional CCM examination video streams. CRISP excludes defocused and discontinuous segments via focus-aware gating, propagates poses through local pairwise registration, and maintains non-redundant spatial coverage with a sparse anchor map; when local temporal continuity is interrupted, the system completes relocalization and subgraph merging through global appearance retrieval followed by geometric verification. The framework prioritizes low-latency coverage feedback during examination while outputting accepted frames, poses, and anchor information to initialize offline fine stitching. To our knowledge, CRISP is the first open-source real-time SNP wide-field stitching framework released for conventional CCM video streams. By lowering the barrier to adoption and reproduction of wide-field stitching, CRISP may help move SNP wide-field imaging from a research tool into routine clinical examination workflows.