Medical Volume Rendering With Segmentation Uncertainty
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Solution Overview
Problem
Existing auto-contouring algorithms for radiation therapy planning require significant human expert verification due to high uncertainty in anatomical region segmentation, which is time-consuming and inefficient.
Innovation Solution
A computer-implemented method that performs segmentation and uncertainty determination on volumetric medical datasets, using machine-learning models to generate segmentation data and uncertainty data, and applies physically based volume rendering to visualize anatomical regions with integrated uncertainty, allowing for efficient review and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If auto-contouring algorithms are used for segmentation, then productivity is improved, but reliability deteriorates due to high uncertainty in anatomical region identification
Solution Approach 1:
The system implements feedback by computing uncertainty metrics from the segmentation results and using this information to guide verification priorities. The uncertainty values are fed back into the workflow to automatically identify regions requiring expert review, creating a closed-loop system that improves both productivity and reliability.
Solution Approach 2:
The patent changes the parameter representation by introducing uncertainty metrics alongside segmentation labels. Instead of only binary segmentation results, the system outputs continuous uncertainty values that quantify the confidence level, enabling differentiated verification strategies based on actual segmentation quality.
2Reliability
If comprehensive verification of all segmentation contours is performed, then reliability is improved, but loss of time increases significantly
Solution Approach 1:
The system applies local quality by differentiating verification requirements across different spatial regions. Instead of uniform verification of all contours, the uncertainty metrics enable identification of specific high-uncertainty regions that require expert attention, while low-uncertainty regions can be accepted automatically, optimizing the verification process locally.
Solution Approach 2:
The patent implements partial action by performing verification only on a subset of contours identified as high-uncertainty regions. Rather than requiring complete verification of all 153 contours, the system selectively applies expert review where it is most needed, reducing overall verification time while maintaining adequate reliability.
3Loss of information
If uncertainty data is integrated into volume rendering, then information completeness is improved, but device complexity increases
Solution Approach 1:
The system merges multiple data types (segmentation labels, uncertainty values, and visual parameters) into a unified volume rendering representation. By combining these datasets and applying integrated transfer functions, the system presents comprehensive information including uncertainty in a single coherent visualization rather than requiring separate displays for each data type.
Solution Approach 2:
The patent implements multi-functionality by designing a unified rendering pipeline that simultaneously visualizes anatomical structures, segmentation results, and uncertainty information. The transfer functions serve multiple purposes: encoding both segmentation labels and uncertainty values, enabling a single rendering system to handle diverse information types without requiring separate specialized tools.
Data Source
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AI summary
A computer-implemented method for rendering a volumetric dataset representing a medical volume is described. The method comprises: performing a segmentation process on a volumetric dataset to determine segmentation data indicative of an association between a given location in the volumetric dataset and a given one of one or more anatomical regions in the medical volume, and performing an uncertainty determination process on the segmentation data to determine uncertainty data indicative of an uncertainty in the association between the given location and the given one of the one or more anatomical regions. The method also comprises performing a volume rendering process on the volumetric dataset, using the uncertainty data, to render an image of the medical volume, including determining a visual characteristic of the image based on the uncertainty in the association between the given location and the given one of the one or more anatomical regions.