2D Convolutional Neural Network for 3D Image Analysis
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Solution Overview
Problem
Existing methods for analyzing three-dimensional image data using deep neural networks are inefficient due to the large number of parameters and long calculation times associated with three-dimensional convolutional neural networks, which occupy significant memory and are slow to learn.
Innovation Solution
The approach involves reconstructing two-dimensional images into three-dimensional data, rotating them to generate additional data, and applying a two-dimensional convolutional neural network to each set of three-dimensional data, combining results from various layers for effective analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional convolutional neural network is used to analyze three-dimensional image data, then the analysis capability is improved, but the number of parameters increases and calculation time becomes longer
Solution Approach 1:
The patent segments the three-dimensional data processing task into multiple two-dimensional processing steps. Instead of applying a single 3D convolutional layer, the method divides the 3D data into multiple 2D slices or projections, processes each slice independently using 2D convolutional neural networks, and then combines the results. This segmentation reduces the computational complexity from O(k³) in 3D convolutions to O(3k²) in multiple 2D convolutions, significantly reducing calculation time while maintaining analysis capability.
Solution Approach 2:
The patent transforms the problem from three-dimensional space to multiple two-dimensional spaces. By projecting or slicing the 3D data along different dimensions (e.g., axial, coronal, sagittal views in medical imaging), the method processes data in 2D planes using efficient 2D convolutional networks, then aggregates the results to achieve 3D analysis. This dimensionality change exploits the efficiency of 2D operations while preserving 3D contextual information.
2Measurement precision
If a three-dimensional convolutional neural network is used to analyze three-dimensional image data, then the analysis capability is improved, but memory usage increases
Solution Approach 1:
The patent segments the three-dimensional data processing task into multiple two-dimensional processing steps. Instead of applying a single 3D convolutional layer, the method divides the 3D data into multiple 2D slices or projections, processes each slice independently using 2D convolutional neural networks, and then combines the results. This segmentation reduces the computational complexity from O(k³) in 3D convolutions to O(3k²) in multiple 2D convolutions, significantly reducing calculation time while maintaining analysis capability.
Solution Approach 2:
The patent transforms the problem from three-dimensional space to multiple two-dimensional spaces. By projecting or slicing the 3D data along different dimensions (e.g., axial, coronal, sagittal views in medical imaging), the method processes data in 2D planes using efficient 2D convolutional networks, then aggregates the results to achieve 3D analysis. This dimensionality change exploits the efficiency of 2D operations while preserving 3D contextual information.
3Adaptability or versatility
If a three-dimensional convolutional neural network is used, then three-dimensional data can be processed directly, but the learning speed becomes slower
Solution Approach 1:
The patent segments the three-dimensional data processing task into multiple two-dimensional processing steps. Instead of applying a single 3D convolutional layer, the method divides the 3D data into multiple 2D slices or projections, processes each slice independently using 2D convolutional neural networks, and then combines the results. This segmentation reduces the computational complexity from O(k³) in 3D convolutions to O(3k²) in multiple 2D convolutions, significantly reducing calculation time while maintaining analysis capability.
Solution Approach 2:
The patent transforms the problem from three-dimensional space to multiple two-dimensional spaces. By projecting or slicing the 3D data along different dimensions (e.g., axial, coronal, sagittal views in medical imaging), the method processes data in 2D planes using efficient 2D convolutional networks, then aggregates the results to achieve 3D analysis. This dimensionality change exploits the efficiency of 2D operations while preserving 3D contextual information.
Data Source
AI summary
An apparatus for image analysis includes: an image acquisition unit for stacking a plurality of two-dimensional image data in a predetermined order; a three-dimensional image generation unit for generating a plurality of three-dimensional data on the basis of different types of multiple items of information for the plurality of two-dimensional image data in a stacked form from the image acquisition unit; and a deep learning algorithm analysis unit for applying a two-dimensional convolutional neural network to each of the plurality of three-dimensional data from the three-dimensional image generation unit, and combining results of applying the two-dimensional convolutional neural network to the plurality of three-dimensional data.