Automated Medical Image Segmentation System
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Solution Overview
Problem
Current methods for medical image segmentation and identification are labor-intensive and reliant on human expertise, requiring extensive time and resources for annotation and model training, with limited efficiency and accuracy due to the need for ground truth data and high numbers of training images.
Innovation Solution
An integrated system for image segmentation that includes a training subsystem to generate and refine machine learning models using annotated data, an evaluation mechanism to assess model performance, and a segmentation subsystem for automated image analysis, allowing continuous improvement and deployment of models based on predefined thresholds and retraining processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and identification methods are used, then expertise and knowledge can be applied to achieve accurate results, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs preliminary automated segmentation using machine learning models before manual refinement. The model pre-processes images by generating initial segmentation contours, which experts then review and adjust. This preliminary action significantly reduces the time required for manual annotation while maintaining accuracy, as experts only need to correct rather than create segments from scratch.
Solution Approach 2:
The patent replaces manual mechanical segmentation processes with automated machine learning-based segmentation systems. Deep learning models automatically generate segmentation results, substituting the manual mechanical process of drawing contours with an automated computational process. This substitution dramatically reduces time consumption while the system allows for quality control through expert review of automated results.
2Productivity
If automated machine learning systems are used, then productivity and efficiency are improved, but the quality of results relies heavily on training data and model performance
Solution Approach 1:
The system implements a feedback mechanism where automated segmentation results are reviewed and corrected by experts, and these corrections are fed back to retrain and improve the model. The system continuously learns from expert corrections, adjusting its parameters and weights to improve accuracy. This feedback loop ensures that productivity gains from automation do not compromise reliability, as the model evolves to match expert-level quality standards.
Solution Approach 2:
The patent merges automated machine learning segmentation with manual expert review into a hybrid system. Rather than choosing between fully automated or fully manual methods, the system combines both approaches: the ML model handles initial segmentation to maintain high productivity, while expert review ensures quality control and reliability. This merging allows the system to achieve both high throughput and high accuracy simultaneously.
3Measurement precision
If extensive annotated training data is collected, then model accuracy and reliability are improved, but the annotation process becomes more labor-intensive
Solution Approach 1:
The system implements self-service annotation where the machine learning model automatically generates segmentation annotations that can be used for training without requiring extensive manual annotation. The model annotates its own training data by generating preliminary segmentation results, which then serve as training examples. This self-service approach dramatically reduces the labor required for data annotation while still building an accurate model, as the system learns from its own automated outputs combined with minimal expert guidance.
Data Source
AI summary
An image segmentation method system, the system comprising: a training subsystem configured to train a segmentation machine learning model using annotated training data comprising images associated with respective segmentation annotations, so as to generate a trained segmentation machine learning model; a model evaluator; and a segmentation subsystem configured to perform segmentation of a structure or material in an image using the trained segmentation machine learning model. The model evaluator is configured to evaluate the segmentation machine learning model by (i) controlling the segmentation subsystem to segment at least one evaluation image associated with an existing segmentation annotation using the segmentation machine learning model and thereby generate a segmentation of the annotated evaluation image, and (ii) forming a comparison of the segmentation of the annotated evaluation image and the existing segmentation annotation. The method includes deploying the trained segmentation machine learning model for use if the comparison indicates that the segmentation machine learning model is satisfactory.