Self-aware Image Segmentation via Parallel Process Consensus
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Solution Overview
Problem
Automated image segmentation in medical imaging tasks, such as urology treatment planning and radiation therapy, often fails to converge to the correct solution, leading to inaccurate results due to initial condition dependencies and the inability to cover all possible image variations, requiring manual supervision for correction.
Innovation Solution
A system that performs multiple, parallel segmentation processes with different initializations, identifies mutually similar results, and combines them using statistical techniques to produce a robust output, while a binary classifier determines image segmentability and applies remedial processes for non-segmentable images, reducing the need for manual supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple parallel segmentation processes are performed with different initializations, then segmentation reliability is improved, but computational complexity increases
Solution Approach 1:
The patent divides the segmentation task into multiple parallel processes, each starting with different initializations. This segmentation of the computational task allows the system to explore multiple potential solutions simultaneously, improving reliability by selecting the best result from diverse initial conditions without requiring a single overly complex segmentation algorithm
Solution Approach 2:
The patent creates multiple copies of the segmentation process running in parallel, each with perturbed initial conditions. Instead of developing one highly complex segmentation algorithm, the system replicates a simpler segmentation process multiple times with varied starting points, then combines results to achieve high reliability
2Measurement precision
If training data encompasses all possible image variations, then segmentation accuracy is improved, but data requirements increase
Solution Approach 1:
The patent applies random perturbations to initial segmentation parameters (such as mesh positions or region growing seeds) to generate diversity in segmentation results. This parameter variation allows the system to handle image variations without requiring separate training data for every possible scenario, as the perturbation mechanism dynamically adapts to different image characteristics
Solution Approach 2:
The patent creates a universal segmentation approach that can handle multiple image variations through a single trained model combined with parallel perturbed processes. Instead of requiring specialized training data for each image type, the system uses one training set to train a base segmentation algorithm, then achieves versatility through multiple parallel executions with different initializations
3Productivity
If automated segmentation is implemented without manual supervision, then productivity is improved, but segmentation accuracy deteriorates
Solution Approach 1:
The patent implements a self-evaluation mechanism where the segmentation system automatically assesses its own results through mutual comparison of parallel segmentation processes. The system identifies consensus regions across multiple parallel executions and flags only uncertain cases for manual review, allowing automated processing of confident cases while maintaining accuracy through self-correction
Solution Approach 2:
The patent incorporates feedback loops where segmentation results from parallel processes are compared and used to refine the final output. The system provides feedback on segmentation uncertainty and automatically adjusts by selecting results from multiple parallel processes, reducing manual supervision needs while maintaining accuracy through automated feedback-driven selection
Data Source
AI summary
The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.