Medical Image Segmentation Models for Accurate Target Region Features
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Solution Overview
Problem
Manual image segmentation in medical imaging, such as MRI and CT, is time-consuming and inefficient, affecting the accuracy and efficiency of subsequent image processing and analysis.
Innovation Solution
A method and system for image segmentation using segmentation models to determine target regions and feature information, including the use of morphological and functional images, and multiple segmentation models to enhance precision and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual image segmentation is used, then segmentation can be performed, but it is time-consuming and inefficient
Solution Approach 1:
The system uses automated segmentation models that perform image segmentation without requiring manual intervention. The segmentation model processes images independently, extracting target regions automatically based on learned patterns and features, thereby eliminating the time-consuming manual segmentation process while maintaining segmentation functionality
Solution Approach 2:
The patent replaces the manual mechanical segmentation process with an automated computational segmentation model. The model uses algorithms and machine learning techniques to automatically identify and segment target regions from medical images, substituting the manual operator's mechanical process with an automated digital system that is both faster and more consistent
2Measurement precision
If manual image segmentation is used, then segmentation can be performed, but accuracy and efficiency of subsequent image processing is affected
Solution Approach 1:
The segmentation model incorporates feedback mechanisms where the model's predictions are continuously refined based on training data and performance metrics. This feedback loop enables the model to improve its segmentation precision over time, ensuring high accuracy in target region identification while maintaining efficient processing speeds through optimized algorithms
Solution Approach 2:
The system optimizes segmentation parameters such as threshold values, feature weights, and model hyperparameters to achieve the best balance between precision and efficiency. By dynamically adjusting these parameters based on image characteristics and processing requirements, the system maintains high segmentation accuracy while improving overall processing efficiency
3Measurement precision
If multiple segmentation models are used, then segmentation precision is enhanced, but system complexity increases
Solution Approach 1:
The patent divides the segmentation task into multiple specialized models, each trained to handle specific types of images or target regions. This segmentation of functionality allows each model to be optimized for its specific domain, improving overall precision while managing complexity through modular architecture where each model can be independently trained, deployed, and scaled
Data Source
AI summary
The present disclosure is related to systems and methods for feature information determination. The method may include obtaining at least one image including a subject. The method may include determining a segmentation result by segmenting the at least one image using at least one segmentation model. The segmentation result may include at least one target region of the subject in the at least one image. The method may include determining feature information of the at least one target region based on at least one parameter of the at least one target region.