Automatic Edge Detection Brush for Medical Image Segmentation
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Solution Overview
Problem
Current methods for generating transducer layouts for tumor treating fields (TTFields) face challenges in accurately segmenting medical images, particularly due to irregular tissue shapes and noise, making the process time-consuming and inefficient.
Innovation Solution
A computer-implemented method for processing medical images using automatic edge detection and segmentation, allowing for the generation of transducer layouts by designating voxels as edges based on user-defined brushes and thresholds, thereby improving the accuracy and efficiency of TTFields application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation methods are used for medical images, then the process allows for detailed manual control and adjustment, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs automatic edge detection and segmentation without requiring manual intervention for each step. The computer-implemented method automatically identifies edges, segments tissues, and generates transducer layouts, allowing the system to serve itself rather than requiring continuous human operation.
Solution Approach 2:
The patent replaces manual mechanical segmentation operations with automated computer-based image processing algorithms. The mechanical act of manually drawing segmentation lines is substituted with automated edge detection algorithms that analyze image data and generate segmentation results computationally.
2Productivity
If automated edge detection is implemented, then the segmentation process becomes faster and more efficient, but the precision and accuracy of tissue boundary detection may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the automated edge detection results are evaluated and refined. The method uses iterative processes that adjust detection parameters based on the quality of edges detected, allowing the system to learn from its own output and improve accuracy while maintaining automated efficiency.
Solution Approach 2:
The patent employs multiple edge detection algorithms with different parameters and thresholds. By changing detection parameters such as gradient thresholds, smoothing factors, and sensitivity levels, the system can optimize edge detection accuracy for different tissue types and image qualities while maintaining automated processing speed.
Data Source
AI summary
A method for processing a medical image of a subject is provided. The method includes presenting on a display a slice through the medical image of the subject. The medical image includes voxels. The method further includes performing automatic edge detection in the slice of the medical image to obtain a segmented slice. The automatic edge detection is based on a user selected voxel in the slice of the medical image. The automatic edge detection is based on a user controllable edge detection brush. Not all of the voxels selected by the edge detection brush are designated as an edge.