Medical Image Project Management Platform with Radiomic Feature Extraction
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Solution Overview
Problem
Current medical image analysis technologies lack integration of radiomics and AI-assisted labeling, which limits the accuracy and efficiency of tumor heterogeneity analysis and diagnostic support, particularly in validating medical image labeling quality and integrating diagnostic information for project management.
Innovation Solution
A medical image project management platform that includes a multi-module management interface for image input and labeling, a radiomic feature extracting module for analyzing labeled images, and an AI training module for establishing an AI-assisting labeling model, along with a labeling validation module using metrics like ASSD, IoU, and DICE coefficients to validate labeling quality, and a text extracting module for natural language processing to categorize diagnostic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiomics and AI-assisted labeling are integrated, then labeling quality and diagnostic accuracy are improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: image processing module, radiomic feature extraction module, AI model training module, and validation module. Each module handles specific tasks independently, making the complex system manageable and maintainable while achieving high labeling quality through specialized processing at each stage
Solution Approach 2:
The platform integrates multiple functions into a unified system that can perform image processing, extract radiomic features, train AI models, validate labeling quality, and manage projects. This multi-functional design eliminates the need for separate systems while maintaining high accuracy through integrated workflows
2Productivity
If automated AI labeling is implemented, then productivity increases, but reliability of labeling may decrease without validation
Solution Approach 1:
The system incorporates a validation module that provides feedback on AI-generated labels by comparing them against ground truth or expert annotations. This feedback loop enables continuous improvement of the AI model's labeling accuracy while maintaining high productivity through automation
Solution Approach 2:
The system performs preliminary processing of medical images and extraction of radiomic features before AI labeling occurs. This preparation ensures that the data is optimized for AI processing, improving the reliability of automated labeling while maintaining efficiency
3Measurement precision
If multi-dimensional patient information is collected, then heterogeneity analysis accuracy is improved, but data management complexity increases
Solution Approach 1:
The data management system segments and organizes multi-dimensional patient information into structured categories: demographic data, imaging data, radiomic features, and clinical outcomes. This segmentation enables precise heterogeneity analysis while simplifying data management through standardized organization
Solution Approach 2:
The radiomic feature extraction module acts as an intermediary that transforms complex imaging data into quantifiable radiomic features. This transformation simplifies the management of multi-dimensional patient information while preserving the accuracy needed for heterogeneity analysis
Data Source
AI summary
The present invention discloses a medical image project management platform comprising a project management module and a radiomic feature extracting module. The project management module comprises a multi-module management interface and a labeling unit. An image is input by the multi-module management interface and received by the labeling unit. A first labeled image and a second labeled image are produced thereafter. The radiomic feature extracting module comprises an analysis unit and a feature extracting module. The analysis unit analyzes the first labeled image and gives the first labeled image a first labeling unit. The analysis unit analyzes the second labeled image and gives the second labeled image a second labeling unit. The radiomic feature extracting unit receives the first and the second labeling units and proceeds radiomic computation to output a radiomic feature.