Automated Tissue Segmentation via Multi-Modal Ensemble Learning
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Solution Overview
Problem
Current radiation treatment for cancer relies heavily on manual analysis of medical images, leading to significant delays and variability in treatment planning, resulting in suboptimal clinical outcomes and radiation toxicity.
Innovation Solution
An automated system for target and tissue segmentation using multi-modal imaging and ensemble machine learning models, which combines outputs from multiple imaging modalities (such as CT, MRI, and PET scans) and machine learning models to provide more reliable tumor and organ delineation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of medical images is used for target and tissue segmentation, then treatment planning can be performed with human expertise, but significant delays occur and inter-practitioner variability leads to suboptimal outcomes
Solution Approach 1:
The patent replaces the mechanical manual analysis system with an automated computer-based image processing system that uses multi-modal imaging and ensemble machine learning models to perform target and tissue segmentation, eliminating human time constraints while maintaining or improving segmentation accuracy
Solution Approach 2:
The system enables self-service automation where the computer automatically performs segmentation without human intervention by integrating multiple imaging modalities and machine learning algorithms, allowing the system to serve itself in completing the segmentation task independently
2Reliability
If manual analysis is used for radiation treatment planning, then clinical judgment can be applied, but high inter-practitioner variability results in suboptimal clinical outcomes and radiation toxicity
Solution Approach 1:
The patent merges multiple imaging modalities (CT, MRI, PET) and multiple machine learning models into a unified ensemble system that processes images through multiple pathways and combines results, thereby improving reliability through diversity while managing complexity through integrated architecture
Solution Approach 2:
The system creates a composite analytical approach by combining different imaging modalities and machine learning algorithms into an ensemble model, where each component contributes unique strengths to achieve more consistent and reliable segmentation outcomes than any single method could provide
3Productivity
If automated segmentation systems are implemented, then treatment planning time is reduced, but achieving high accuracy requires multi-modal imaging and ensemble models increasing system complexity
Solution Approach 1:
The patent segments the overall segmentation task into multiple parallel processing streams, each handling different imaging modalities and machine learning models independently, then combines results through an ensemble approach, thereby managing complexity through modular decomposition while maintaining high productivity
Data Source
AI summary
Methods and systems for automated target and tissue segmentation using multi-modal imaging and ensemble machine learning models are provided herein. In some embodiments, a method comprises: receiving a plurality of medical images, wherein each of the plurality of medical images includes a target and normal tissue; combining the plurality of medical images to align the target and normal tissue across the plurality of medical images; inputting the combined medical images into each of a plurality of machine learning models; receiving, in response to the input, an output from each of the plurality of machine learning models; combining the results of the plurality of machine learning models; generating a final segmentation image based on the combined results of the plurality of machine learning models; assigning a score to each segmented target and normal tissue; and sorting the segmented targets and normal tissues based on the scores.