Medical Image Processing Apparatus Using Learned Model for Time Phase Classification
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Solution Overview
Problem
Current medical imaging technologies face challenges in accurately classifying time phases of contrast agent distribution in tumors, which is crucial for real-time diagnosis, often relying on operator expertise and subjective interpretation.
Innovation Solution
A medical image processing apparatus that utilizes a learned model to classify time phases based on contrast image data from ultrasonic, CT, or MRI scans, employing deep learning techniques to generate needfulness data for storing and diagnosing contrast image data, enabling objective and efficient tumor diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operator expertise and subjective interpretation are used to classify time phases, then diagnostic accuracy may be improved through human judgment, but diagnosis time increases and consistency decreases
Solution Approach 1:
The patent replaces the mechanical system of manual operator interpretation with an automated image processing apparatus that uses contrast image data and learned models to objectively classify time phases. The processing circuitry automatically determines time phase information without requiring operator expertise, thereby reducing diagnosis time while maintaining or improving classification accuracy through consistent algorithmic application.
2Adaptability or versatility
If manual classification methods are used, then flexibility in interpretation is maintained, but measurement precision and objectivity deteriorate
Solution Approach 1:
The patent substitutes manual classification with an automated system that processes contrast image data through learned models to generate objective time phase classifications. This eliminates subjective interpretation variability while maintaining adaptability through the use of trained algorithms that can be adjusted based on different diagnostic requirements and patient data.
3Measurement precision
If deep learning techniques are employed, then classification accuracy improves, but device complexity increases
Solution Approach 1:
The patent implements deep learning techniques within the image processing apparatus, where the processing circuitry contains learned models trained on contrast image data. While this increases device complexity compared to simple threshold-based methods, it significantly improves time phase classification accuracy by enabling the system to learn complex patterns and relationships in the medical imaging data that simpler algorithms cannot detect.
Data Source
AI summary
The medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to acquire contrast image data generated by imaging a subject. The processing circuitry is configured to input the acquired contrast image data to a learned model to generate a time phase data classified according to a contrast state of a lesion area with a contrast agent included in the acquired contrast image data, the learned model being for generating the time phase data based on the acquired contrast image data.