Tumor Segmentation via 3D CNN Slice Processing
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Solution Overview
Problem
Current methods for tumor segmentation in brain imaging, particularly for glioblastomas, are suboptimal due to reliance on 2D measurements, inter-observer variability, and the complexity of accurately delineating tumors in 3D images.
Innovation Solution
The use of machine learning techniques, specifically deep convolutional neural networks (CNNs), for autonomous tumor delineation and segmentation, which involves filtering normal brain tissue to extract tumor features and using 3D image patches to reduce computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D image acquisition is used for tumor delineation, then measurement precision and tumor identification accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the 3D brain volume into multiple 2D slices for processing. Each slice is independently analyzed by the neural network to identify tumor regions, then the results are integrated to form the complete 3D tumor segmentation. This segmentation approach maintains high measurement precision while reducing computational complexity compared to processing the entire 3D volume at once.
2Measurement precision
If multiple imaging modalities are used in parallel, then tumor identification accuracy is improved, but ease of operation and radiologist workload worsen
Solution Approach 1:
The system implements automated tumor segmentation using deep learning models that process multiple imaging modalities (T1, T1c, T2, T2c) simultaneously. The neural network automatically identifies and segments tumor regions across all modalities without requiring radiologists to manually analyze each modality separately. This self-service approach maintains high tumor identification accuracy while significantly reducing radiologist workload and eliminating inter-observer variability.
3Measurement precision
If whole-volume brain samples are used for tumor identification, then measurement precision is improved, but productivity and processing efficiency deteriorate
Solution Approach 1:
The patent divides the whole brain volume into multiple 2D slices along different anatomical planes (axial, coronal, sagittal). The neural network processes these slices independently and efficiently, then integrates the results to produce the complete 3D tumor segmentation. This segmentation strategy maintains high measurement precision by preserving spatial relationships while dramatically improving processing efficiency compared to analyzing the entire volume as a single unit.
Solution Approach 2:
The patent transforms the 3D volume processing problem into a series of 2D slice processing tasks. By processing images in 2D slices rather than as a single 3D volume, the system achieves better computational efficiency and memory utilization while maintaining accurate 3D tumor reconstruction through integration of slice-level segmentations.
4Ease of operation
If 2D measurements are used for treatment response assessment, then ease of operation is improved, but measurement precision and information completeness worsen
Solution Approach 1:
The patent enhances 2D slice-based analysis by integrating results across multiple anatomical planes (axial, coronal, sagittal) to reconstruct complete 3D tumor geometry. This approach maintains the operational simplicity of 2D image analysis while achieving the measurement precision and information completeness of full 3D characterization, providing accurate tumor volume, shape, and spatial extension data.
Data Source
AI summary
Systems and methods of tumor segmentation are receiving data having dimensions of a first size and a first kernel size. A residual volume is produced from the input volume. A first, second, and third intermediate volume are produced by from convolving a first, second, and third dimension volume of the residual volume to 1. A first global volume is produced from sums of the residual volumes. A downsampled volume is produced from the input volume. A residual downsampled volume is produced from the downsampled volume. A first, second, and third intermediate downsampled volume is produced from convolving a first, second, and third dimension volume of the downsampled volume to 1. A second global volume is produced from sums of the intermediate downsampled volumes. The second global volume is upsampled. An output volume is produced from integrating the first global volume and the second global volume.