Seed Point Derivation from Contours for Image Segmentation Correction
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Solution Overview
Problem
Conventional automatic segmentation methods in medical imaging, such as CT and MRI scans, often produce erroneous results, necessitating manual editing processes that require users to select seed points, which can be time-consuming and inefficient.
Innovation Solution
A method that automatically derives foreground and background seed points from contours drawn on pre-segmented images using a Graph Cuts or Random Walker method, with a weighting factor based on distance, allowing for iterative refinement until a satisfactory segmentation is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automatic segmentation methods are used, then segmentation can be performed without manual intervention, but the results contain erroneous markings and are not reliable
Solution Approach 1:
The system performs preliminary segmentation automatically, then uses contour-based seed point generation as a preliminary correction step before final segmentation. The contour drawing and seed point derivation are performed as preliminary actions to guide subsequent accurate segmentation, resolving the contradiction by preparing corrected seed points before the main segmentation process.
Solution Approach 2:
The patent introduces contour-based seed points as an intermediary between the automatic segmentation and the final corrected segmentation. These seed points serve as a mediator that bridges the unreliable automatic segmentation results with the desired accurate segmentation, allowing the system to correct errors through the intermediary seed point generation process.
2Reliability
If manual seed point selection is required for editing segmentation, then segmentation can be corrected, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service by automatically generating seed points from user-drawn contours without requiring manual selection of individual seed points. The contour-based algorithm automatically derives foreground and background seed points, allowing the system to serve itself in the seed point generation process, thus correcting segmentation efficiently without time-consuming manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual seed point selection process with an automated computational system. Instead of requiring users to manually click and select seed points, the system uses contour-based algorithms to automatically generate seed points, substituting the manual mechanical process with an automated computational mechanism that is both accurate and efficient.
3Measurement precision
If users individually paint seed points, then precise seed point placement is achieved, but the operation becomes complex and labor-intensive
Solution Approach 1:
The patent segments the seed point generation task into two parts: contour drawing (simple user input) and seed point derivation (automated computation). By segmenting this way, the system maintains precision in seed point placement while simplifying the user operation to just drawing contours, eliminating the complexity of manually selecting and painting individual seed points.
Solution Approach 2:
The contour serves as an intermediary that simplifies the interaction between the user and the segmentation system. Instead of requiring precise manual painting of seed points, the user simply draws a contour, and the system uses this contour as an intermediary to automatically derive the precise seed points, thus maintaining accuracy while greatly simplifying the operation.
Data Source
AI summary
A method for processing an object in image data includes the steps of drawing a contour on a pre-segmentation of an object in image data, generating at least one seed point on the pre-segmentation from an intersection of the contour and the pre-segmentation, providing a weighting factor between the seed points and the pre-segmentation, and segmenting the pre-segmentation using the seed points and the weighting factor to generate a new pre-segmentation.