3D Object Segmentation in Medical Radiation Images
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Solution Overview
Problem
Current methods for segmenting 3D objects in medical radiation images, such as CT or MR images, are often specific to certain types of objects and require significant user interaction, leading to potential inaccuracies due to the need for precise operations.
Innovation Solution
A method that detects the contour of a 3D object in 2D slice images, generates 2D segmentation masks, and interpolates these masks to create a segmented 3D volume, requiring minimal user interaction by allowing rough contour drawing and using image processing techniques like gradient computation and Canny Edge Detector for automatic adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated segmentation tools for specific organs or tumors are used, then segmentation accuracy for those specific objects is improved, but the device complexity and lack of versatility increase
Solution Approach 1:
The patent implements a universal segmentation tool that can segment any 3D object in medical images regardless of organ or tumor type. The system uses generic image processing algorithms (edge detection, region growing, level sets) that work across different anatomical structures, eliminating the need for multiple dedicated tools while maintaining versatility
2Extent of automation
If semi-automated segmentation tools are used, then automation level is improved, but user interaction requirements increase
Solution Approach 1:
The segmentation algorithm performs self-correction and automatic refinement of contours through iterative optimization. The system automatically adjusts segmentation boundaries by analyzing image gradients and intensity variations, reducing the need for manual user adjustments while maintaining ease of operation
3Manufacturing precision
If extensive user interaction is required for precise segmentation, then segmentation precision is improved, but time consumption and user error potential increase
Solution Approach 1:
The system performs preliminary automatic segmentation to generate initial contours, which are then automatically refined using image processing algorithms. This preliminary automated action reduces the time and effort required for precise segmentation while minimizing user error potential
Solution Approach 2:
The segmentation algorithm incorporates feedback mechanisms that automatically evaluate and adjust contour accuracy based on image intensity gradients and regional homogeneity. This closed-loop feedback system maintains high precision without requiring extensive manual intervention
Data Source
AI summary
On the basis of user input a set of contour points of a 3D object is detected in a number of 2D slice images representing the 3D object. Next a 2D object is segmented in each of the slice images by the set of contour points so as to obtain segmentation masks. Finally, by interpolation between computed segmentation masks, the 3D object is segmented.