Medical Image Display Device for Interstitial Lung Disease Tracking
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Solution Overview
Problem
Accurately performing comparative observation over time for interstitial lung disease using medical images is challenging due to the difficulty in accurately displaying region changes for multiple symptoms in lung images.
Innovation Solution
A medical image display device and method that classify target regions in multiple medical images into case regions, generate mapping images, calculate the center-of-gravity position and movement of these regions, and display changes, enabling precise tracking of region changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning is applied to classify pixels in three-dimensional medical images into multiple types of structures, then the classification accuracy and recognition rate of feature amounts are improved, but the device complexity and computational processing requirements increase significantly
Solution Approach 1:
The patent segments the complex deep learning process into distinct functional modules: a classification unit that divides target regions into multiple case regions, a mapping image generation unit that creates visual representations, and a change calculation unit that computes differences. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining high classification accuracy through specialized processing in each segment.
2Measurement precision
If multiple types of case regions are classified and displayed in medical images showing various symptoms, then the ability to perform accurate comparative observation over time is improved, but the difficulty of detecting and measuring region changes for each symptom increases
Solution Approach 1:
The patent introduces mapping images as an intermediary representation between the original medical images and the change analysis. The mapping image generation unit creates simplified visual mappings that highlight specific case regions, serving as a mediator that translates complex multi-symptom images into easier-to-analyze formats. This intermediary step significantly reduces the difficulty of detecting and measuring region changes while preserving the ability to perform accurate comparative observations across multiple symptoms and time points.
Data Source
AI summary
A classification unit classifies a lung region, which is included in each of two three-dimensional images having different imaging times for the same subject, into a plurality of types of case regions. A mapping image generation unit generates a plurality of mapping images corresponding to the three-dimensional images by labeling each of the classified case regions. A change calculation unit calculates the center-of-gravity position of each case region for case regions at corresponding positions in the mapping images, and calculates the movement amount and the movement direction of the center-of-gravity position between the mapping images as a change of each case region. A display control unit displays information regarding the change on a display.