In dentistry, microscopes have become indispensable optical devices for high-quality treatment and micro-invasive surgery, especially in the field of endodontics. Recent machine vision advances enable more advanced, real-time applications including but not limited to dental video deblurring and workflow analysis through relevant metadata obtained by instrument motion trajectories. To this end, the proposed work addresses dental video deblurring and instrument segmentation in a Multi-task Learning fashion, leveraging spatio-temporal adaptive kernels via a recurrent design. The task-specific branches of our architecture employ the responses of those kernels to recover sharper video frames and yield the dental instrument segmentation mask. We demonstrate that the proposed method improves deblurring while retaining segmentation performance under a low computational footprint.
Authors
- Efkleidis Katsaros link open in new tab ,
- dr inż. Anna Jezierska,
- dr inż. Daniel Węsierski link open in new tab
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1109/hsi52170.2021.9538656
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2021
