ML-Based Medical Image Registration Quality Assessment and Correction
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Solution Overview
Problem
Medical image registration is a time-consuming process, especially with large deformations or abnormalities, where automatic registration algorithms struggle to provide robust results, and there is no effective way to assess the quality of registration, often requiring manual correction.
Innovation Solution
A mechanism is developed to automatically predict and correct image registration failures using machine learning algorithms, learning from registration histories, and performing region of interest analysis, symmetric registration, and key organ segmentation to improve registration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic registration algorithms are used for medical images with large deformations or abnormalities, then processing speed is improved, but registration accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by training a machine learning model (random forest classifier) on registration histories to predict registration quality. The system automatically assesses registration results and provides feedback for correction when failures are predicted, thereby maintaining high accuracy while using automatic algorithms for fast processing.
Solution Approach 2:
The system performs self-correction of registration failures through automated detection and correction mechanisms. When the machine learning model predicts a registration failure, the system automatically initiates correction procedures without requiring manual intervention, enabling self-service operation that maintains accuracy while preserving processing speed.
2Manufacturing precision
If manual correction is performed for registration failures, then registration accuracy is improved, but time consumption increases
Solution Approach 1:
The system implements self-service by automatically detecting registration failures using a machine learning model trained on registration histories. When failures are detected, the system automatically initiates correction procedures, eliminating the need for manual intervention in most cases and significantly reducing time consumption while maintaining accuracy.
Solution Approach 2:
The feedback mechanism continuously monitors registration quality and automatically triggers correction only when necessary. This selective feedback approach ensures high accuracy by correcting failures while minimizing time loss by avoiding unnecessary manual corrections for successful registrations.
3Productivity
If no quality assessment mechanism is implemented, then processing efficiency is maintained, but registration reliability deteriorates
Solution Approach 1:
The patent implements a quality assessment feedback mechanism using a machine learning model that predicts registration quality based on historical data. This automated assessment provides reliable quality control while maintaining processing efficiency by quickly evaluating registration results and triggering corrections only when necessary, rather than requiring exhaustive manual verification of every registration.
Data Source
AI summary
A medical registration training component executing within a medical registration system performs a training medical registration operation on a pair of medical studies. Responsive to the medical registration training system determining that the training medical registration operation succeeds, the medical registration training system records a medical registration instance for the pair of medical studies in a medical registration history and marks the medical registration instance as a positive instance in the medical registration history. Responsive to the medical registration training system determining that the training medical registration operation requires correction, the medical registration training system records a medical registration instance for the pair of medical studies in the medical registration history and marks the medical registration instance as a negative instance in the medical registration history. The medical registration training system trains a failure prediction machine learning model based on the medical registration history using machine learning such that the failure prediction machine learning model predicts whether a new medical registration operation will require correction. Responsive to the failure prediction machine learning model predicting that the new medical registration operation will require correction, the mechanism takes steps to automatically correct the new medical registration operation.