Deep Attention Neural Network for Medical Image Segmentation from Raw Data
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Solution Overview
Problem
Traditional medical image segmentation methods introduce noise and information loss due to separate reconstruction and segmentation tasks, with existing end-to-end learning frameworks being unsuitable for complex scenarios and requiring fully-sampled images during training.
Innovation Solution
A machine-learned neural network is trained to segment directly from raw k-space data using an attention network and recurrent segmentation, focusing on segmentation rather than reconstruction, with training data generated by medical imaging simulation from digital phantoms to overcome noise and artifact influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image reconstruction is performed before segmentation, then image quality is improved, but noise and artifacts are introduced that degrade segmentation quality
Solution Approach 1:
The patent combines the reconstruction and segmentation tasks into a single end-to-end neural network framework. The network takes raw k-space data as input and directly outputs segmentation maps, eliminating the separate reconstruction step that introduces noise and artifacts. This unified approach allows the model to learn optimal feature representations for segmentation without being constrained by reconstruction quality.
2Measurement precision
If separate reconstruction and segmentation tasks are used, then reconstruction accuracy is improved, but information loss occurs that degrades segmentation performance
Solution Approach 1:
The patent performs segmentation directly from raw k-space data without requiring prior reconstruction. By skipping the reconstruction step entirely, the method preserves all original information contained in the raw measurements, including subtle features that might be lost or distorted during reconstruction. The neural network learns to extract segmentation-relevant information directly from the raw data representation.
3Reliability
If existing end-to-end learning frameworks are used for segmentation, then segmentation performance is improved, but they require fully-sampled images during training which limits applicability to complex scenarios
Solution Approach 1:
The patent changes the fundamental parameter of data representation by operating directly in the raw k-space domain rather than requiring fully-sampled image space data. This parameter change enables the model to handle undersampled data and complex imaging scenarios where fully-sampled images are unavailable. The network learns robust features that generalize across different sampling conditions and imaging protocols.
Data Source
AI summary
Various approaches provide improved segmentation from raw data. Training samples are generated by medical imaging simulation from digital phantoms. These training samples provide raw measurements, which are used to learn to segment. The segmentation task is the focus, so image reconstruction loss is not used. Instead, an attention network is used to focus the training and trained network on segmentation. Recurrent segmentation from the raw measurements is used to refine the segmented output. These approaches may be used alone or in combination, providing for segmentation from raw measurements with less influence of noise or artifacts resulting from a focus on reconstruction.