Segmentation Mask Refinement Using User-Defined Fuzzy Constraints
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Solution Overview
Problem
Conventional methods for manually post-processing automatically predicted segmentation masks in medical imaging are cumbersome and time-consuming, lacking intuitiveness, necessitating a more efficient approach to refine segmentation masks based on user inputs.
Innovation Solution
A computer-implemented method that refines a first segmentation mask by incorporating user-defined areas with fuzzy inputs, using an algorithm configured to penalize violations of user-provided constraints, thereby adapting the mask semi-automatically and reducing the need for precise modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual post-processing of segmentation masks is performed using conventional methods, then the segmentation mask can be refined, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system automatically refines the segmentation mask by processing user hints through a computer-implemented algorithm, eliminating the need for manual pixel-by-pixel adjustment. The algorithm autonomously incorporates user constraints into the mask refinement process, making the system self-serving rather than requiring continuous manual intervention.
Solution Approach 2:
The patent replaces manual mechanical adjustment of segmentation masks with an automated computer algorithm. Instead of manually editing the mask, users provide hints that are processed computationally to generate the refined mask, substituting manual operations with automated computational processes.
2Measurement precision
If manual post-processing of segmentation masks is performed, then the segmentation mask can be refined, but the process lacks intuitiveness
Solution Approach 1:
The system changes the interaction parameter from manual pixel adjustment to fuzzy region hints. Instead of requiring precise manual input, users can provide broader region constraints, and the algorithm translates these into accurate mask refinements, making the operation more intuitive and easier to perform.
3Productivity
If automated algorithms generate segmentation masks, then processing speed is improved, but the masks require extensive manual review and adaptation
Solution Approach 1:
The system incorporates user hints as feedback into the automated segmentation process. Users provide constraints or corrections, and the algorithm uses this feedback to refine the mask automatically, creating a feedback loop that maintains high processing speed while incorporating necessary adjustments.
Solution Approach 2:
The segmentation process becomes dynamic by allowing users to provide hints that adapt the automated mask generation in real-time. The system transitions from a static automated process to a dynamic interactive process where user input continuously refines the output, making the automated system more adaptable to specific needs.
Data Source
AI summary
Examples of the disclosure relate to a method of refining a segmentation mask, the method including providing a first segmentation mask associated with a region of interest in an image, providing a user input comprising a user-defined area of the image, and obtaining a second segmentation mask by refining the first segmentation mask based on image data of the image using a computer-implemented algorithm, wherein the algorithm is configured to penalize a violation of the user input.