Intelligent Boundary Delineation of Tissue Regions Using Perfusion Models
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Solution Overview
Problem
Current methods for detecting and diagnosing diseases, particularly cancer, rely heavily on subjective human observation of contrast agent perfusion patterns in tissues, which are time-consuming and limited in accuracy, making it challenging to differentiate between healthy and unhealthy tissue effectively.
Innovation Solution
The implementation of intelligent boundary delineation and classification of regions of interest in organisms using machine learning operations on time series data from multispectral video streams, applying perfusion models to quantify and classify spatio-temporal behavior of contrast agents, enabling real-time, objective assessment of tissue perfusion patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective human observation methods are used to detect and diagnose diseases, then medical expertise and judgment can be applied, but the process is time-consuming and limited in accuracy
Solution Approach 1:
The patent replaces the mechanical system of human observation and diagnosis with an automated image processing and machine learning system. The system captures images, processes them through algorithms, and automatically classifies tissue regions, eliminating the time-consuming manual observation process while maintaining or improving diagnostic accuracy through objective quantitative analysis.
Solution Approach 2:
The system enables self-service diagnosis by automatically performing the entire diagnostic workflow from image capture to classification without requiring continuous human intervention. The machine learning models autonomously analyze the images and provide diagnostic results, allowing the system to serve itself in the diagnostic process.
2Measurement precision
If manual observation of contrast agent perfusion patterns is used, then tissue regions can be assessed, but the process is limited in accuracy and requires extensive human expertise
Solution Approach 1:
The patent segments the complex diagnostic task into distinct processing stages: image capture, preprocessing, feature extraction, machine learning classification, and result generation. Each stage handles a specific aspect of the analysis, making the overall complex system manageable and improvable through targeted optimizations at each segment.
Solution Approach 2:
The system employs universal machine learning models that can classify multiple types of tissue regions (healthy, unhealthy, boundary regions) using the same perfusion pattern analysis framework. This multi-functional approach allows a single system to handle various diagnostic scenarios without requiring separate specialized tools for each tissue type.
3Productivity
If real-time classification of tissue regions is implemented using machine learning, then diagnostic speed and accuracy are improved, but computational resources and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images and pre-training machine learning models before actual diagnostic use. Feature extraction algorithms are prepared in advance, and models are trained on representative datasets beforehand, so that during real-time diagnosis, only the final classification step needs to be executed, significantly reducing real-time computational energy requirements.
Data Source
AI summary
Embodiments for implementing intelligent boundary delineation of a region of interest of an organism in two spatial dimensions in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. One or more regions of interest having one or more perfusion patterns may be identified from the time series data. Boundaries of the one or more regions of interest may be delineated into at least two spatial dimensions, wherein the boundaries of the one or more regions of interest include one or more selected labels.