Optical-Property Medical Image Rendering with Uncertainty Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern AI-based segmentation tools in medical imaging face challenges in achieving high-performance, high-quality three-dimensional volume rendering without latency and disruptive visual artifacts, especially in real-time applications like surgical guidance.
Innovation Solution
A method for volume and surface rendering using optical properties per sampling points, incorporating an uncertainty indicator to scale randomization of sampling points, allowing for high-quality rendering without complex pre-processing, and enabling real-time applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional smoothing approaches (smoothed distance transform, specialized interpolation, smooth surface extraction) are used to achieve high-quality rendering, then rendering quality is improved, but processing time increases due to additional pre-processing steps
Solution Approach 1:
The patent extracts and eliminates the complex pre-processing steps (smoothed distance transform, specialized interpolation, smooth surface extraction) that cause latency. Instead of applying these time-consuming operations, the invention directly uses the binary segmentation mask with uncertainty indicators in the rendering pipeline, achieving both speed and quality without the harmful pre-processing overhead.
Solution Approach 2:
The patent changes the rendering approach by incorporating uncertainty indicators as a new parameter that guides sampling density. By adjusting the number of sampling points based on uncertainty levels rather than applying fixed smoothing operations, the system achieves high rendering quality while maintaining real-time performance, resolving the contradiction between quality and speed.
2Productivity
If fast rendering algorithms are used to reduce latency, then processing speed is improved, but visual artifacts appear at boundaries of segmentation classes
Solution Approach 1:
The patent applies local quality by varying the number of sampling points based on local uncertainty levels. In regions with high uncertainty (boundary areas), the system increases sampling density to capture visual details accurately, while using fewer samples in low-uncertainty regions. This localized adaptation maintains rendering speed overall while ensuring high visual quality where needed, eliminating boundary artifacts without sacrificing performance.
3Manufacturing precision
If additional pre-processing steps are applied to improve rendering quality, then visual accuracy is improved, but system complexity increases
Solution Approach 1:
The patent removes the complex pre-processing pipeline (smoothed distance transform, specialized interpolation, smooth surface extraction) that increases system complexity. By directly rendering the binary segmentation mask with uncertainty indicators integrated into the rendering process itself, the system achieves high visual accuracy without the cumbersome multi-step pre-processing overhead, simplifying the overall system architecture.
Data Source
Figure 1~2
Figure 3A~3C
Figure 4A~5C
AI summary
A technique for volume rendering, and/or surface rendering, of a medical imaging data set based on optical properties per sampling points is provided. A method comprises a step of receiving an uncertainty indicator per voxel, and/or per surface element, in relation to a segmentation mask of, and/or an anatomical structure comprised in, a medical imaging data set. The method further comprises a step of scaling a randomization of one or more sampling points based on the received uncertainty indicator, and a step of determining at least one optical property per sampling point. The method still further comprises a step of rendering the volume based on the voxels, and/or the surface based on the surface elements. The rendering is based on the determined at least one optical property per sampling point.