Medical Image Segmentation Interpolation via Voxel Similarity Scoring
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Solution Overview
Problem
Current medical image processing techniques lack efficient interpolation methods for segmentation values, leading to jagged boundaries and reduced image quality due to the inability to mix segmentation labels, which are often stored in complex compressed data structures, making interpolation costly and resource-intensive.
Innovation Solution
A medical image processing apparatus and method that selects a relevant voxel for a sampling point based on the difference in interpolated image data values and distance from neighboring voxels, using a similarity score combining the difference and distance terms to determine a segmentation value, thereby interpolating image data values and improving segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation values are interpolated using complex compressed data structures, then segmentation accuracy is improved, but computational cost and resource consumption increase significantly
Solution Approach 1:
The patent segments the complex interpolation problem into two distinct parts: (1) interpolate image data values using standard techniques, and (2) select the segmentation value from neighboring voxels based on the interpolated image data. This segmentation allows each part to be optimized independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces interpolated image data values as an intermediary between the raw image data and the segmentation values. This intermediary guides the selection of segmentation values at sampling points, enabling accurate segmentation interpolation without directly interpolating the discrete segmentation labels themselves, thus avoiding the computational burden of full segmentation interpolation.
2Productivity
If no interpolation of segmentation values is performed, then computational efficiency is maintained, but image quality deteriorates with jagged boundaries and aliasing
Solution Approach 1:
The patent uses the interpolated image data values as a guide to select segmentation values from neighboring voxels, effectively copying the smooth transitions achieved in image data interpolation to the segmentation data selection process. This avoids direct manipulation of segmentation labels while achieving similar smoothing effects.
Solution Approach 2:
The patent changes the approach from interpolating segmentation label values directly to selecting segmentation values based on interpolated image data parameters. By using the interpolated image data as a selection criterion rather than interpolating the segmentation data itself, the method achieves smooth transitions without the computational cost of full segmentation interpolation.
3Manufacturing precision
If segmentation labels are converted to colors and interpolated, then segmentation smoothness is improved, but resource consumption increases
Solution Approach 1:
The patent extracts only the necessary information (interpolated image data values) from the full interpolation process to guide segmentation value selection. By taking out just the image data interpolation step and using it to select segmentation values, the method achieves segmentation smoothness without extracting and processing the full segmentation data through interpolation, reducing resource consumption.
Data Source
AI summary
A medical image processing apparatus comprises processing circuitry configured to: obtain medical image data comprising or representative of image data values and segmentation values for pixels or voxels of a volume; specify a sampling point within the volume; select a relevant pixel or voxel for the sampling point from a set of pixels or voxels neighboring the sampling point, wherein the selecting is based on a difference of an interpolated image data value at the sampling point and an image data value for each of the neighboring pixels or voxels and a distance between the sampling point and each of the neighboring pixels or voxels; and determine a segmentation value for the sampling point based on a segmentation value at a position of the selected relevant pixel or voxel.