FloVMos: Optical Flow-based Medical Video Mosaicking
Biomedical imaging modalities often require a trade-off among resolution, field of view (FOV), and acquisition speed. Video mosaicking offers a strategy to overcome this limitation by computationally stitching sequential high-resolution frames into a wide-FOV composite. However, existing methods struggle with non-rigid deformations, and modality-specific artifacts arising in clinical and research imaging. Here, we present FloVMos, a generalizable, optical-flow-based deep learning framework for real-time video mosaicking across diverse biomedical imaging modalities. FloVMos achieves robust, pixel-level registration by fine-tuning an optical flow model on synthetic training data with ground...
| Authors | Jinyang Liu, Sandesh Ghimire, Chaman Singh et al. |
|---|---|
| Primary category | eess.IV |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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