ROI Segmentation for Low-Clutter 3D Medical Visualization
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Solution Overview
Problem
Existing medical visualization systems for image-guided surgery struggle with noise and background clutter, particularly in 3D models, which can obscure the relevant anatomical structures and hardware, leading to reduced image quality and user experience.
Innovation Solution
The system employs automated image segmentation to differentiate regions of interest from background, using multiple thresholds to generate 3D models that focus on specific anatomical structures and hardware while suppressing irrelevant tissue, and utilizes head-mounted displays for augmented reality visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 3D models are displayed with background features (soft tissue), then anatomical context is provided, but noise and background clutter increase obscuring relevant structures
Solution Approach 1:
The patent applies automated image segmentation to divide the 3D medical image into distinct regions of interest (bone, cartilage, soft tissue, hardware) and background. This segmentation enables selective display of only the relevant anatomical structures and surgical hardware while excluding background clutter and noise, thereby resolving the contradiction between providing anatomical context and reducing visual interference.
Solution Approach 2:
The system extracts and separates the relevant content (bone structures, cartilage, surgical hardware) from the irrelevant background content. By taking out only the necessary elements for the surgical procedure and displaying them in isolation, the system eliminates background clutter and noise while preserving the essential anatomical information needed for the procedure.
2Manufacturing precision
If multiple thresholds are applied for segmentation, then 3D model clarity is improved, but processing time increases
Solution Approach 1:
The patent performs automated image segmentation and thresholding in advance, before the surgical procedure begins. By pre-processing the medical images to create the 3D models with multiple thresholds applied, the system prepares the visualizations ahead of time, reducing or eliminating processing time during the actual surgical procedure while maintaining high model clarity.
Solution Approach 2:
The system uses automated algorithms to perform the segmentation and thresholding processes without requiring manual intervention. The automated nature of the processing allows for efficient batch processing of multiple thresholds, achieving high 3D model clarity while minimizing the time investment required from the surgical team.
3Loss of information
If 3D models include all tissue types, then complete anatomical information is provided, but irrelevant content (hardware, soft tissue) is displayed
Solution Approach 1:
The patent applies different display characteristics and thresholds to different local regions and tissue types within the 3D model. By assigning local quality attributes to specific structures (highlighting bone and hardware while dimming or excluding soft tissue), the system provides complete anatomical information where needed while filtering out irrelevant content, achieving selective visibility based on surgical relevance.
Data Source
AI summary
A computer-implemented method includes obtaining a three-dimensional (3D) image of a region of a body of a patient, the 3D image having feature values. The 3D image is segmented to define one or more regions of interest (ROIs). At least one region of interest (ROI) feature threshold is determined. A background feature threshold is determined. A 3D model is generated from the 3D image based on the determined at least one ROI feature threshold, the determined background feature threshold, and the segmentation. The 3D model is output for display to a user.