Automated Region of Interest Matching in Serial Medical Images
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Solution Overview
Problem
Current methods for evaluating tumor progression in serial medical images are manual, time-consuming, and prone to errors, especially when dealing with multiple objects of interest, such as metastases and tumor recurrence, lacking fully automated object matching across longitudinal image sets.
Innovation Solution
A computer-implemented method determines the correspondence between regions of interest in different medical images by calculating the overlap ratio between image representations and applying a threshold to establish matching, using image registration and volume correlation algorithms to map and compare anatomical body parts across images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation methods are used to assess tumor progression in serial medical images, then clinical expertise can be applied to interpret data, but the process becomes very time-consuming and error-prone especially for large patient pools
Solution Approach 1:
The system enables automated self-evaluation of tumor progression by having the computer automatically collect, match, and analyze serial medical images and associated data, replacing manual clinical evaluation with autonomous computational processes that maintain accuracy while dramatically reducing time consumption
Solution Approach 2:
The patent replaces the manual mechanical process of clinical evaluation with automated computational methods including image registration algorithms, object matching algorithms, and data enrichment systems that process medical images and extract tumor progression information without human intervention
2Measurement precision
If manual collection and enrichment of patient data is performed for clinical research, then data accuracy can be maintained, but the process is very time-consuming in particular for large pool of patients
Solution Approach 1:
The system automatically performs data collection and enrichment tasks by having the computer retrieve serial medical images, match regions of interest across images, extract tumor characteristics, and compile research data without requiring manual data entry or processing, thereby maintaining precision while dramatically improving productivity
Solution Approach 2:
The system creates and processes digital copies of medical images and patient data through automated workflows, using image registration and object matching algorithms to replicate and analyze tumor regions across multiple serial images, enabling efficient large-scale data processing while maintaining data accuracy
3Productivity
If automated object matching algorithms are implemented across longitudinal image sets, then time consumption and human error are reduced, but the system complexity increases
Solution Approach 1:
The system segments the complex task of tumor progression analysis into distinct automated components: image registration module, object matching module, data extraction module, and data enrichment module, allowing each component to be optimized independently while working together to solve the overall problem efficiently
Solution Approach 2:
The system uses parameter-based approaches including threshold values for object matching, registration parameters for image alignment, and configurable criteria for tumor identification, allowing the complex automated system to be tuned and optimized for different clinical scenarios while maintaining high productivity
Data Source
AI summary
Disclosed is a computer-implemented method of determining a correspondence between a region of interest as it appears in a first digital medical patient image and as it appears in a second digital medical image. The correspondence is determined by calculating the ratio of overlap of the region of interest with a data object defining an anatomical body part in the first image and the second image and determining whether the larger of the two ratios exceeds a threshold. If the threshold is exceeded, the method assumes that the appearances in the two images describe the same region of interest.