2D CNN Hematoma Expansion Prediction via 3D-to-2D Slicing
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Solution Overview
Problem
Three-dimensional (3D) convolutional neural networks (CNNs) face computational burdens and the curse of dimensionality, limiting their application in medical imaging tasks like hematoma expansion prediction due to high computational intensity and memory requirements, often restricting batch sizes and efficiency.
Innovation Solution
The method involves converting 3D non-contrast computed tomography (NCCT) images into 2D images, using a 2D U-Net for hematoma segmentation, and then employing a 2D classification CNN to predict hematoma expansion by extracting a fixed-size 3D region of interest (ROI) from the segmented hematomas, thereby reducing computational demands while maintaining prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D CNNs are used to process 3D medical images, then prediction accuracy can be maintained, but computational burden and memory requirements increase substantially
Solution Approach 1:
The patent segments the 3D medical image into multiple 2D slices and processes each slice independently using 2D CNNs. This segmentation approach reduces the computational complexity from 3D to 2D level while maintaining the ability to capture spatial relationships through the slice sequence, thereby reducing computational burden while preserving prediction accuracy.
Solution Approach 2:
The patent transforms the problem from 3D spatial processing to 2D processing by converting 3D volumetric data into 2D slices. This dimensional reduction allows the use of computationally lighter 2D CNNs instead of 3D CNNs, significantly reducing memory requirements and computational energy while maintaining predictive performance through the preservation of anatomical information across slices.
2Measurement precision
If 3D CNNs are used to process 3D medical images, then prediction accuracy can be maintained, but memory space requirements increase substantially
Solution Approach 1:
The patent divides the 3D image volume into multiple 2D slices, allowing the system to process and store data in smaller 2D units rather than requiring large 3D arrays in memory. This segmentation reduces the peak memory space requirement while maintaining the ability to reconstruct and analyze the full 3D structure through the sequence of 2D slices.
Solution Approach 2:
By converting 3D volumetric data into 2D slices, the patent reduces the memory footprint from three-dimensional arrays to two-dimensional images. This dimensional transformation significantly decreases the memory space required for storing and processing image data while preserving the essential anatomical information needed for accurate hematoma expansion prediction.
3Measurement precision
If 3D CNNs are used to process 3D medical images, then prediction accuracy can be maintained, but training data requirements increase substantially
Solution Approach 1:
The patent segments the 3D imaging data into 2D slices, which allows for more efficient use of training data. By processing 2D slices independently, the system can leverage existing 2D medical imaging datasets more effectively and reduce the amount of 3D training data required, as the 2D slices can be extracted and trained from smaller or existing data collections.
Solution Approach 2:
The transformation from 3D to 2D processing reduces the dimensionality of the training data requirement. This allows the system to train on 2D image data which is less data-intensive than 3D volumetric data, thereby reducing the quantity of training data needed while maintaining prediction accuracy through the preservation of spatial relationships across multiple 2D slices.
4Productivity
If 2D CNNs are used to process 3D medical images, then computational efficiency improves, but the ability to capture 3D spatial relationships may be compromised
Solution Approach 1:
The patent segments the 3D volume into multiple 2D slices that are processed independently by 2D CNNs. This segmentation allows 2D CNNs to process each slice with high computational efficiency while the sequence of slices collectively captures the full 3D spatial relationships, achieving both efficiency and accuracy.
Solution Approach 2:
The patent uses multiple 2D slices to represent the 3D structure, effectively replacing the need for 3D processing with a series of 2D processed images. This approach maintains computational efficiency of 2D CNNs while capturing 3D spatial relationships through the temporal or sequential arrangement of the 2D slices, preserving anatomical context and spatial connectivity.
Data Source
AI summary
A method and apparatus include receiving a three dimensional (3D) non-contrast computed tomography (NCCT) image of a head including a hematoma. A plurality of two dimensional (2D) images of the head including the hematoma are generated using the 3D NCCT image of the head including the hematoma. A plurality of 2D hematoma images are generated using a first 2D convolutional neural network (CNN) based on the plurality of 2D images. A 3D region of interest (ROI) that encompasses the hematoma is identified based on the plurality of 2D hematoma images. A plurality of 2D images that correspond to the ROI are generated. A hematoma expansion (HE) prediction score is determined using a second CNN based on the plurality of 2D images that correspond to the ROI. The HE prediction score is provided.