Neural Network Motion Estimation Using Feature and Motion Pyramids
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Solution Overview
Problem
Conventional motion estimation techniques for anatomical structures, such as the human heart, are time-consuming and lack accuracy due to significant segmentation work and resemblance issues between tissues and organs, leading to suboptimal results.
Innovation Solution
A neural network-based system that uses a feature pyramid and motion pyramid to estimate motion between source and reference images, with a student neural network trained by a teacher neural network for progressive motion compensation, reducing the need for extensive annotation and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion estimation techniques are used, then processing can be completed with standard methods, but the accuracy is reduced when tissues or organs around a target anatomical structure bear a resemblance to the target structure
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component between the feature extraction and motion estimation processes. This attention mechanism selectively weights different regions of the image, allowing the system to focus computational resources on discriminative features that distinguish the target anatomical structure from surrounding tissues, thereby improving accuracy without proportionally increasing overall complexity
Solution Approach 2:
The patent applies local quality by implementing region-specific processing through the attention mechanism, where different parts of the image receive different levels of processing intensity. The attention weights dynamically adjust to emphasize regions containing the target structure while suppressing regions with similar-appearing but irrelevant tissues, enabling accurate motion estimation without uniformly increasing complexity across the entire image
2Productivity
If conventional motion estimation techniques are used, then the method is simpler, but the process is very time-consuming
Solution Approach 1:
The patent segments the motion estimation process into multiple parallel components: a coarse motion estimation branch and a refined motion estimation branch with attention mechanism. This segmentation allows the system to first quickly establish approximate motion fields and then selectively refine only the critical regions, significantly reducing the overall time required compared to conventional uniform processing methods
Solution Approach 2:
The patent applies partial action by implementing the attention mechanism only in regions where it is most beneficial - specifically where tissue resemblance creates ambiguity. Rather than applying complex processing uniformly across the entire image, the system concentrates computational effort on problematic regions, reducing total processing time while maintaining accuracy where it matters most
3Measurement precision
If a student neural network is trained using a teacher neural network with progressive motion compensation, then motion field prediction accuracy is improved, but the training process becomes more complex
Solution Approach 1:
The patent implements a student-teacher neural network architecture where the student network is essentially a copy of the teacher network structure. The student network learns by copying the teacher's knowledge through knowledge distillation, where the teacher's soft labels and attention patterns are used to guide the student's training. This copying approach enables the student to achieve high prediction accuracy while the training complexity is managed through the structured knowledge transfer process
Data Source
AI summary
Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed using a feature pyramid and/or a motion pyramid that correspond to multiple image scales. The motion estimation may be performed using neural networks and parameters that are learned via a training process involving a student network and a teacher network pre-pretrained with abilities to apply progressive motion compensation.