Leveraging spatio-temporal features for joint deblurring and segmentation of instruments in dental video microscopy

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 work...

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Bibliographic Details
Main Authors: Katsaros, Efklidis, Jezierska, Anna, Wesierski, Daniel
Format: Conference Proceeding
Language:English
Subjects:
dental microscope
Dentistry
instrument segmentation
Microscopy
Motion segmentation
multi-task learning
Object segmentation
spatio-temporal features
Streaming media
Surgery
Trajectory
video deblurring
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Summary: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.
ISSN:2158-2254
DOI:10.1109/HSI52170.2021.9538656