Medical Image Segmentation with Non-Image Data
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Solution Overview
Problem
Current image segmentation methods in medical fields often rely solely on imaging information, leading to low accuracy in identifying regions of interest (ROIs) in medical images such as CT, MRI, and PET images.
Innovation Solution
A system and method that incorporates non-image information, such as user-related, biological, and image acquisition information, using a multichannel neural network-based image segmentation model to enhance the accuracy of ROI determination by transforming non-image information into a format that can be processed alongside the image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only imaging information is used for image segmentation, then the system complexity remains low, but the segmentation accuracy deteriorates
Solution Approach 1:
The patent combines multiple information sources (imaging information and non-image information) into a unified segmentation framework. The system integrates medical images with non-image data such as biological information, user information, and acquisition information, processing them together through a joint segmentation model to achieve improved accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The patent introduces non-image information as an intermediary element that bridges the gap between limited imaging data and comprehensive diagnostic needs. This intermediary information (biological data, user data, acquisition parameters) acts as a mediator that enhances the segmentation process by providing additional contextual constraints and guidance, thereby improving accuracy without requiring direct modification of the imaging system itself.
2Productivity
If manual segmentation is performed, then flexibility in handling complex cases is maintained, but time consumption and user variability increase
Solution Approach 1:
The patent implements an automated segmentation system that performs the segmentation task independently without requiring manual intervention. The system uses self-contained algorithms that automatically process medical images and non-image information to generate segmentation results, thereby eliminating user variability and significantly improving processing efficiency while maintaining consistent accuracy across different cases.
Solution Approach 2:
The patent incorporates feedback mechanisms where the segmentation system continuously refines its results by utilizing non-image information as feedback constraints. The system uses biological information, user information, and acquisition information as feedback signals to guide and correct the segmentation process, ensuring consistent and reliable results while maintaining high productivity through automated iterative refinement.
Data Source
AI summary
Systems and methods for image segmentation are provided. A system may obtain a first image of a subject. The system may obtain non-image information associated with at least one of the first image or the subject. The system may further determine a region of interest (ROI) of the first image based on the first image, the non-image information, and an image segmentation model.