Marginal Space Deep Neural Networks for Anatomical Pose Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing anatomical object detection methods in medical images, such as those using marginal space learning (MSL), are not robust when anatomical objects exhibit large variations in anatomy, shape, or appearance, particularly in complex medical imaging modalities like CT, MRI, and ultrasound, due to the reliance on handcrafted image features which are inefficient in capturing complex distributions.
Innovation Solution
The use of deep neural networks that directly input image patches from training data to learn high-level domain-specific features, dividing the parameter space into marginal search spaces of increasing dimensionality for more accurate anatomical object detection, employing discriminative and regression functions to calculate probabilities and displacement vectors for pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If handcrafted image features are used for anatomical object detection, then the detection process is simpler, but the robustness and accuracy deteriorate when anatomical objects exhibit large variations in anatomy, shape, or appearance
Solution Approach 1:
The patent replaces handcrafted feature extraction methods with deep neural networks that automatically learn features from raw image data. This substitution enables the system to capture complex anatomical variations and patterns that handcrafted features cannot represent, significantly improving detection robustness while maintaining computational feasibility through efficient network architectures.
Solution Approach 2:
The patent transforms the feature representation parameters from fixed handcrafted descriptors to adaptive learned features through deep neural networks. By changing the parameter space from predefined features to data-driven feature representations, the system can dynamically adapt to various anatomical variations, improving reliability without excessive complexity.
2Measurement precision
If deep neural networks are used to learn high-level features directly from image patches, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the parameter space into multiple marginal search spaces with increasing dimensionality. Instead of searching the entire high-dimensional space at once, the system divides it into manageable subsets (e.g., position, orientation, scale separated), training separate deep neural networks for each marginal space. This segmentation reduces computational complexity while maintaining high detection accuracy through focused learning in each subspace.
Solution Approach 2:
The patent transforms the high-dimensional parameter space search problem into a sequence of lower-dimensional marginal space problems. By changing from a single high-dimensional search to multiple low-dimensional searches, the system reduces computational burden while preserving detection accuracy through the hierarchical structure of marginal spaces.
3Measurement precision
If the parameter space is divided into marginal search spaces with increasing dimensionality, then detection precision improves, but the number of networks to train increases
Solution Approach 1:
The patent segments the parameter space into meaningful marginal spaces (position, orientation, scale) and trains dedicated networks for each. This segmentation allows precise estimation of each parameter by specialized networks, improving overall pose estimation precision. The modular structure also enables efficient training and deployment compared to a single monolithic network.
Solution Approach 2:
Each deep neural network in the marginal space framework is designed to be universal within its specific search space, learning features that are transferable across different anatomical structures and imaging modalities. This universality reduces the need for extensive customization and retraining, mitigating the complexity increase from having multiple networks.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.