Synthetic Depth From Monocular Endoscopes for Surgical Robotics
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Solution Overview
Problem
Surgical robotic systems face challenges in providing high-quality stereoscopic endoscopic video due to the impracticality of using endoscopes with two optical channels, which are larger and cumbersome, especially in minimally-invasive surgeries, leading to impaired depth perception and surgeon fatigue.
Innovation Solution
A system that predicts stereoscopic video from monocular endoscopes using algorithms, generating a live stereoscopic endoscope video feed by filling in occluded regions with predicted images and overlaying confidence shading based on prediction certainty, leveraging neural networks and depth estimation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereoscopic endoscopes with two optical channels are used, then depth perception and surgical precision are improved, but the endoscope size and device complexity increase making them impractical for minimally-invasive surgery
Solution Approach 1:
The patent creates a virtual copy of the stereoscopic viewing experience by using computer vision algorithms to generate synthetic depth information from monocular endoscope images. The system reconstructs 3D scene understanding and synthesizes a second viewpoint image, providing stereoscopic depth perception without requiring a physical second optical channel in the endoscope.
Solution Approach 2:
The patent replaces the mechanical/optical system of dual-lens endoscopes with a computational system. Instead of using two physical optical paths and lenses, the system uses image processing algorithms, neural networks, and computer vision techniques to synthesize depth information and create stereoscopic images from a single monocular camera feed.
2Measurement precision
If stereoscopic endoscopes with two optical channels are used, then depth perception is improved, but the endoscope physical size increases making it impractical for small incisions
Solution Approach 1:
The system creates a virtual stereoscopic view by computing a synthetic second viewpoint image from the monocular feed, eliminating the need for a physically larger dual-lens endoscope. This allows use of small, minimally-invasive endoscopes while still providing immersive 3D depth perception through computational synthesis.
3Device complexity
If monocular endoscopes are used, then endoscope size and device complexity are reduced, but depth perception and surgical precision are impaired leading to surgeon fatigue
Solution Approach 1:
The patent introduces computational algorithms and computer vision processing as an intermediary between the monocular endoscope and the surgeon's view. This intermediary layer synthesizes depth information and generates stereoscopic images, bridging the gap between simple monocular hardware and the need for depth perception without requiring complex dual-lens hardware.
Solution Approach 2:
The system transforms the visual information parameters by converting 2D monocular images into 3D stereoscopic images through computational processing. By changing the representation parameters from flat 2D images to depth-encoded 3D images with confidence shading, the system provides enhanced depth perception from simple monocular hardware.
4Reliability
If confidence shading is overlaid on predicted regions, then surgical safety is improved by indicating prediction certainty, but image processing complexity increases
Solution Approach 1:
The system implements feedback by overlaying confidence shading on synthesized regions based on the reliability of depth predictions. This visual feedback mechanism informs the surgeon about the certainty of generated depth information, allowing them to trust or question the virtual depth cues in different regions of the image.
Data Source
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AI summary
A surgical robotic system includes an image processing device configured to receive an endoscopic video feed and generate a stereoscopic video feed with confidence shading overlays on display. The confidence shading is based on a level of confidence associated with uncertain regions within images making up the stereoscopic video feed.