Medical Image Processing Device Region Extraction
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Solution Overview
Problem
Existing medical image processing technologies face high computational burdens when detecting tissue structures from three-dimensional image data, leading to increased processing time and resource requirements, especially when using convolutional neural networks.
Innovation Solution
A medical image processing device and method that reduces computational complexity by extracting specific regions from three-dimensional images and applying a machine learning algorithm to these regions for structure detection, allowing for efficient processing and maintaining high detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks are used to detect tissue structures from three-dimensional image data, then detection accuracy is improved, but computational burden increases
Solution Approach 1:
The patent applies segmentation by dividing the three-dimensional image data into multiple two-dimensional images before processing. This allows the convolutional neural network to process smaller, more manageable units rather than the entire volumetric dataset at once, thereby reducing computational burden while maintaining detection accuracy through systematic processing of segmented regions.
Solution Approach 2:
The patent transforms the three-dimensional image data into multiple two-dimensional images, effectively reducing the dimensionality of the input data for the convolutional neural network. This dimensionality change simplifies the computational complexity while preserving the essential structural information needed for accurate tissue structure detection.
2Measurement precision
If convolutional neural networks are used to detect tissue structures from three-dimensional image data, then detection accuracy is improved, but processing time increases
Solution Approach 1:
By segmenting the three-dimensional image data into multiple two-dimensional images, the patent enables parallel processing of smaller units, which reduces the overall processing time compared to processing the entire volumetric dataset sequentially through the convolutional neural network.
Solution Approach 2:
The conversion from three-dimensional to two-dimensional representation reduces the computational complexity and processing time required for structure detection, while still maintaining accurate detection results through the systematic analysis of multiple two-dimensional slices.
3Measurement precision
If three-dimensional image data is processed to detect tissue structures, then detection accuracy is improved, but the amount of data to be processed increases
Solution Approach 1:
The patent segments the large three-dimensional image dataset into multiple smaller two-dimensional images, making the data more manageable and reducing the memory and storage requirements while enabling efficient processing through the convolutional neural network.
Solution Approach 2:
By reducing the data from three-dimensional to two-dimensional format, the patent significantly decreases the quantity of data that needs to be processed, stored, and transmitted, while maintaining the essential structural information needed for accurate tissue structure detection.
Data Source
AI summary
A medical image processing device is configured to process data of a three-dimensional image of a biological tissue. The medical image processing device includes a controller configured to: acquire, as an image acquisition step, a three-dimensional image of a tissue; extract, as an extraction step, a first region from the acquired three-dimensional image, the first region being a part of the three-dimensional image; and acquire, as a first structure detection step, a detection result of a specific structure of the tissue in the extracted first region by inputting the first region into a mathematical model that is trained by a machine learning algorithm to output a detection result of a specific structure that is shown in an image input into the mathematical model.