Volumetric Medical Image Organ Identification via Voxel Sampling
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Solution Overview
Problem
Current medical imaging techniques for identifying organs in volumetric images are computationally intensive and require significant resources, often necessitating additional hardware, which is not practical in all settings, and previous methods for reducing computational burden, such as fine granular segmentation, add complexity to database design and communication protocols.
Innovation Solution
A framework that samples voxels from volumetric medical images, skipping at least one voxel between sampled voxels, and uses a trained classifier to identify the type of organ, reducing data processing while maintaining reliable identification through neighborhood information consideration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full segmentation of all organs is performed, then organ identification accuracy is improved, but computational time and resources increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the volumetric medical image into smaller processing units (voxels) and further into neighborhoods. Instead of processing the entire volume at once, the method segments the data into manageable chunks that can be processed independently and efficiently, reducing overall computational time while maintaining identification accuracy.
Solution Approach 2:
The patent implements partial action by sampling only a subset of voxels (e.g., one out of every k voxels) rather than processing all voxels. This selective sampling approach reduces computational burden significantly while still providing sufficient information for accurate organ identification through the trained classifier.
2Loss of time
If fine granular organ segmentation is used, then computational time is reduced, but additional hardware requirements increase
Solution Approach 1:
The patent uses copying by creating a simplified representation of the medical image data through voxel sampling. Instead of requiring powerful hardware to process the full-resolution volumetric data, the method creates a downsampled version (copy) with fewer voxels that retains essential information for organ identification, enabling processing on standard hardware.
Solution Approach 2:
The patent applies parameter changes by modifying the resolution and density of the processed data. By changing the sampling rate (skipping k-1 voxels out of every k), the method transforms the data parameters to reduce computational requirements while maintaining the essential characteristics needed for accurate organ identification through the classifier.
3Productivity
If voxel sampling with skipping is applied, then data processing load is reduced, but identification accuracy may deteriorate
Solution Approach 1:
The patent applies merging by combining information from multiple sampled voxels into neighborhood representations. Instead of losing information through sampling, the method merges the characteristics of neighboring voxels to create comprehensive feature representations that maintain identification accuracy despite processing fewer individual voxels.
Solution Approach 2:
The patent implements feedback through the trained classifier that learns from sampled voxel patterns and adjusts its decision-making process. The classifier receives feedback from the sampled data and refines its organ identification based on the statistical patterns and relationships learned from the voxel neighborhoods, maintaining accuracy even with reduced input data.
Data Source
AI summary
A framework for identifying a type of organ in a volumetric medical image. The framework may include receiving a volumetric medical image, the volumetric medical image comprising at least one organ or portion thereof, and further receiving a single point of interest within the volumetric medical image. Voxels are sampled from the volumetric medical image, wherein at least one voxel is skipped between two sampled voxels. The type of organ is identified at the single point of interest by applying a trained classifier to the sampled voxels.