Interactive 3D Medical Image Segmentation via GPU-Accelerated 2D-to-3D Coordinate Conversion
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Solution Overview
Problem
Current methods for 3-D medical image segmentation, particularly for tooth regions, face challenges such as limited success in contour estimation and difficulty in identifying threshold values, leading to incomplete or inaccurate segmentation results.
Innovation Solution
The use of GPU-based technology with a consolidated viewing window for multi-planar projections allows users to enter geometric primitives in 2-D space, which are converted to 3-D for volume segmentation, facilitating more intuitive and efficient contouring and segmentation of anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated contour estimation methods are used for tooth segmentation, then processing speed is improved, but segmentation accuracy deteriorates due to limited success in contour extraction
Solution Approach 1:
The system performs preliminary automated contour estimation to provide an initial segmentation result, then uses this as a starting point for interactive refinement. The preliminary action includes automatic threshold selection and initial contour generation, which speeds up the overall process while allowing subsequent manual correction to improve accuracy.
Solution Approach 2:
The system implements feedback by displaying the automated contour estimation results to the user and allowing interactive modification. The user can adjust the contour by dragging control points or adding/removing regions, and the system continuously updates the segmentation based on user input, creating a feedback loop that improves accuracy while maintaining efficiency.
2Measurement precision
If manual verification and correction procedures are used for segmentation, then segmentation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary automated segmentation using threshold-based methods and region growing algorithms to generate an initial contour estimate. This preliminary action provides a reasonable starting point that reduces the amount of manual correction needed, thereby maintaining accuracy while minimizing the time investment required from the user.
Solution Approach 2:
The system allows the user to perform partial manual correction only where needed rather than requiring complete manual redrawing of contours. The interactive modification enables selective adjustment of specific regions or control points, reducing the overall time commitment while still achieving accurate segmentation where it matters most.
3Productivity
If threshold-based segmentation methods are used, then processing speed is improved, but segmentation reliability deteriorates due to difficulty in identifying proper threshold values
Solution Approach 1:
The system performs self-service by automatically selecting threshold values using algorithms that analyze the image data distribution. The automated threshold selection includes methods such as Otsu's method or iterative optimization, which enable the system to adapt to different image characteristics without requiring manual threshold tuning, thereby maintaining both speed and reliability.
Solution Approach 2:
The system dynamically adjusts threshold parameters based on the specific characteristics of each image or region being segmented. Rather than using fixed threshold values, the system adapts thresholds to local intensity distributions and image quality, improving reliability across different scanning conditions while maintaining computational efficiency.
4Ease of operation
If conventional 2-D slice-by-slice contouring is used, then ease of operation is maintained, but productivity deteriorates due to time-consuming manual tracing
Solution Approach 1:
The system merges multiple 2-D slice contouring operations into a single interactive 3-D contouring operation. By representing the contour as a 3-D surface with control points that span multiple slices simultaneously, the user can modify the contour in one place and have the changes automatically propagated to related slices, dramatically improving efficiency while maintaining the simplicity of graphical interaction.
Solution Approach 2:
The system transitions from 2-D slice-by-slice contouring to 3-D volumetric contouring by introducing a third dimension (the depth or slice index) to the contour representation. This allows the contour to be manipulated as a unified 3-D structure rather than separate 2-D slices, enabling faster modification while preserving the intuitive graphical interface that users are familiar with from 2-D imaging.
Data Source
AI summary
A method for segmenting an object in a volume image, executed at least in part on a computer, renders the volume image data to a two-dimensional display screen showing first, second, and third mutually orthogonal planes in the two-dimensional rendering. One or more operator instructions that identify a plurality of seed points on the rendered volume image are accepted. Two-dimensional coordinates of the identified seed points on the display screen are converted to three-dimensional seed-point coordinates relating to the first, second, and third mutually orthogonal planes a segmentation operation is performed on the volume image according to the converted three-dimensional seed-point coordinates. Segmentation results are displayed on the display screen, relative to the first, second, and third mutually orthogonal planes.