Monocular Endoscopic 3D Modeling for Depth-Guided Navigation
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Solution Overview
Problem
Conventional endoscopic systems face challenges in accurately navigating internal bodily lumens due to limited depth perception from monocular imaging, relying on pre-operative imaging methods that are inaccurate and require extensive analysis, or complex hardware that is inefficient in real-time surgical environments.
Innovation Solution
A computational model is trained using supervised learning on synthetic images with depth ground truths and domain adversarial training on real images to generate depth images and confidence maps, enabling the creation of 3D anatomical models for improved navigation using existing monocular endoscopes without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-operative diagnostic images (CT images) are used for navigation, then navigation assistance is provided, but extensive pre-operative analysis is required and motion of lungs during procedure cannot be compensated resulting in inaccuracy
Solution Approach 1:
The system performs preliminary action by training computational models on synthetic images with ground truth depth information before the actual procedure. This pre-training enables the model to quickly process real-time endoscopic images during the procedure without requiring extensive pre-operative analysis, thus resolving the contradiction between preparation time and real-time performance
Solution Approach 2:
The system creates synthetic copies of anatomical structures from CT images with embedded ground truth depth information. These synthetic images serve as training data that teaches the model accurate depth estimation without requiring the model to directly process real patient images during pre-operative analysis, reducing time while maintaining accuracy
2Ease of operation
If vision-based navigation techniques are used, then navigation assistance is provided, but additional device hardware (sensors) and complex data manipulation are required limiting effectiveness in real-time surgical environment
Solution Approach 1:
The system replaces mechanical sensor-based depth measurement systems with a computational model that estimates depth from standard monocular endoscopic images. This substitution eliminates the need for additional depth sensors and complex hardware while providing real-time navigation assistance through software-based depth estimation
Solution Approach 2:
The computational model performs self-service by automatically estimating depth and generating navigation information from standard endoscopic images without requiring additional sensors or complex external data manipulation systems. The model processes images directly and generates depth maps independently
3Device complexity
If monocular endoscopic images are used, then simple imaging is provided, but inadequate depth perception makes navigation difficult and successful procedure dependent on operator experience
Solution Approach 1:
The system changes parameters by transforming standard monocular images into enhanced representations with estimated depth information through computational modeling. The model learns to infer depth parameters from single images, effectively converting 2D image data into 3D spatial understanding without changing the physical imaging system
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.