Medical Image Tilt Reduction for Accurate Data
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Solution Overview
Problem
Existing medical image processing technologies face accuracy issues when dealing with tomographic images of tissues that have large tilts relative to the main direction, leading to decreased accuracy in medical data output by mathematical models trained by machine learning algorithms.
Innovation Solution
A medical image processing device and method that performs a tilt-reduction process on tomographic images to align tissue layers with the main direction before inputting them into a mathematical model trained by a machine learning algorithm, thereby improving the accuracy of medical data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a mathematical model trained by machine learning algorithm is used to process tomographic images with large tilts, then medical data can be acquired automatically, but the accuracy of medical data output decreases
Solution Approach 1:
The patent applies a tilt-reduction process as a preliminary action before the mathematical model processes the tomographic image. By pre-aligning the tissue layers to be parallel to the main direction through image processing, the input image is prepared in advance to match the training conditions of the mathematical model, thereby maintaining high accuracy while preserving automated processing.
Solution Approach 2:
The tilt-reduction process acts as an intermediary between the raw tomographic image and the mathematical model. This intermediate processing step transforms the input image by reducing tilts, making it compatible with the model's expected input format and improving the model's ability to extract accurate medical data without requiring retraining.
2Measurement precision
If the mathematical model is retrained with more training images to handle tilted layers, then accuracy can be improved, but the complexity and time required for model development increases
Solution Approach 1:
Instead of retraining the model with additional tilted images, the patent applies tilt-reduction as a preliminary processing step to the input image. This approach maintains the original model's accuracy without requiring complex retraining processes, thereby avoiding increased device complexity and development time.
Solution Approach 2:
The patent changes the parameters of the input image (specifically, the orientation/tilt parameters) through the tilt-reduction process, transforming the image into a format that matches the model's training conditions. This parameter transformation approach is simpler than changing the model's training data or structure.
3Measurement precision
If tilt-reduction process is applied to tomographic images, then accuracy of medical data output is improved, but additional processing time is required
Solution Approach 1:
The tilt-reduction process applies partial action by focusing only on the specific aspect of image correction needed (tilt reduction) rather than complete image reprocessing. This targeted approach improves accuracy while minimizing additional processing time compared to more comprehensive image processing methods.
Data Source
AI summary
A medical image processing device processes tomographic image data of a living tissue. The medical image processing device includes a control unit that includes at least one processor and at least one memory storing computer program code. The computer program code, when executed by the at least one processor, causes the at least one processor to: acquire a tomographic image in which a layer of the living tissue appears; perform a tilt-reduction process on the acquired tomographic image to reduce a tilt of the layer of the living tissue with respect to a main direction; and acquire medical data by inputting, into a mathematical model, a tilt-reduced image that is the tomographic image on which the tilt-reduction process was performed.