Monocular Endoscopic 3D Anatomical Modeling for Depth-Guided Navigation
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Solution Overview
Problem
Conventional endoscopic systems face challenges in localization and navigation due to inadequate depth perception from monocular imaging, leading to reliance on pre-operative imaging and complex hardware, which are inaccurate and inefficient in real-time surgical environments.
Innovation Solution
A computational model is trained using supervised learning on synthetic images with ground truths and domain adversarial training on real images to generate depth images and confidence maps, enabling accurate 3D anatomical model generation from monocular endoscopic images without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If monocular endoscopic imaging is used, then the imaging system remains simple and minimally invasive, but depth perception and localization accuracy deteriorate
Solution Approach 1:
The patent creates virtual copies of the endoscopic scene by generating synthetic depth images and 3D anatomical models from monocular input images. A computational model synthesizes depth information and 3D spatial relationships that would normally require complex hardware, effectively copying the functionality of multi-camera or structured light systems using purely computational methods.
Solution Approach 2:
The patent transforms 2D monocular images into 3D anatomical models by inferring depth information and spatial relationships. The computational model generates depth maps and 3D point cloud representations, adding the depth dimension to the flat 2D images to enable accurate localization and navigation without adding physical imaging dimensions.
2Measurement precision
If pre-operative diagnostic images and tracking systems are used for navigation, then localization accuracy may be improved, but the procedure requires extensive pre-operative analysis and cannot compensate for organ motion
Solution Approach 1:
The computational model processes real-time endoscopic images to generate depth information and 3D anatomical models autonomously, without requiring external tracking systems or pre-operative imaging. The system serves itself by extracting all necessary spatial information directly from the monocular endoscopic feed, eliminating dependence on separate navigation infrastructure.
Solution Approach 2:
The patent enables dynamic adaptation to changing anatomical conditions by processing real-time images during the procedure. Unlike static pre-operative models, the computational model continuously generates updated 3D representations that reflect current organ positions and motions, allowing the system to adapt dynamically throughout the procedure.
3Speed
If vision-based navigation techniques are used, then real-time feedback is provided, but additional device hardware and complex data manipulation are required
Solution Approach 1:
The computational model performs multiple navigation functions using a single processing pipeline: it generates depth maps, constructs 3D anatomical models, provides localization information, and enables navigation guidance all from the same monocular image input. This multi-functional approach eliminates the need for separate hardware systems for each navigation task.
Solution Approach 2:
The patent replaces mechanical and hardware-based navigation systems with a computational approach. Instead of using additional cameras, sensors, or tracking hardware, the system uses image processing algorithms and neural networks to extract all necessary spatial and navigational information from standard endoscopic images.
Data Source
AI summary
A diagnostic imaging process and system may operate to generate a three-dimensional anatomical model based on monocular color endoscopic images. In one example, an apparatus may include a processor and a memory coupled to processor. The memory may include instructions that, when executed by the processor, may cause the processor to access a plurality of endoscopic training images comprising a plurality of synthetic images and a plurality of real images, access a plurality of depth ground truths associated with the plurality of synthetic images, perform supervised training of at least one computational model using the plurality of synthetic images and the plurality of depth ground truths to generate a synthetic encoder, and perform domain adversarial training on the synthetic encoder using the real images to generate a real image encoder for the at least one computational model. Other embodiments are described.