Anatomy-Aware Motion Estimation via Variational Autoencoder
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Solution Overview
Problem
Conventional deep learning-based motion estimation techniques in medical applications rely heavily on image content and require significant annotation efforts, leading to accuracy issues due to similar tissue appearances and difficulties in obtaining ground truth data.
Innovation Solution
A neural network-based motion estimation system is trained using a variational autoencoder to learn the anatomy of a target structure, constraining its outputs and optimizing parameters with shape priors, allowing for minimally supervised training with limited annotated data, and generating anatomically correct motion predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning-based motion estimation techniques are used, then the system can estimate motion using image content, but the accuracy is affected by tissues with similar image appearances and the system requires significant annotation efforts
Solution Approach 1:
The system performs preliminary segmentation to identify the target anatomical structure before motion estimation. This preliminary action creates a region of interest mask that guides the motion estimation process, reducing the impact of similar-appearing tissues and decreasing the need for extensive annotations during training
Solution Approach 2:
The system introduces an intermediary segmentation module that acts as a bridge between the input images and motion estimation. This intermediary creates anatomically constrained flow fields by first segmenting the target structure and then estimating motion only within the segmented region, improving accuracy while reducing annotation requirements
2Ease of manufacture
If conventional motion estimation systems are trained with limited annotated data, then training cost is reduced, but the accuracy of motion estimation deteriorates due to lack of ground truth data
Solution Approach 1:
The system performs self-supervised learning by using its own segmentation outputs as pseudo-ground truth for training the motion estimation module. The segmentation network automatically provides annotated data without requiring external annotation efforts, enabling the system to train accurately with minimal external annotated data
Solution Approach 2:
The system performs preliminary segmentation to generate pseudo-ground truth masks that are then used to supervise the motion estimation training. This preliminary action creates the necessary training signals without requiring external annotators, reducing training data preparation effort while maintaining accuracy
3Reliability
If the system constrains output based on shape prior using VAE, then anatomically unrealistic motion is reduced, but the system complexity increases due to additional VAE component
Solution Approach 1:
The system merges the VAE-based shape prior module with the motion estimation network into a unified architecture. The VAE encoder and decoder are integrated with the motion estimation modules, allowing shared feature representations and reducing overall system complexity despite adding anatomical constraints
Solution Approach 2:
The VAE model serves multiple functions: it learns the shape prior distribution, generates anatomically plausible segmentation masks, and provides constraints for motion estimation. This multi-functionality reduces the need for separate components, offsetting the complexity increase from adding the VAE
4Measurement precision
If the system performs additional anatomy correction operations during processing or post-processing, then motion accuracy is improved, but the processing time and computational cost increase
Solution Approach 1:
The system performs anatomy-aware motion estimation as a preliminary action that inherently produces anatomically correct results through the VAE-constrained training. This eliminates the need for subsequent correction operations, reducing processing time while maintaining accuracy
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 utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.