Marginal Space Deep Neural Networks for Anatomical Object Detection
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Solution Overview
Problem
Existing anatomical object detection methods in medical images, such as Marginal Space Learning (MSL), are not robust for complex anatomical structures with large variations in anatomy, shape, or appearance, as they rely on handcrafted image features that are inefficient in capturing complex distributions and are computationally complex for large 2D+time or 3D volumetric images.
Innovation Solution
The use of marginal space deep neural networks that divide the parameter space into series of search spaces with increasing dimensionality, training a deep neural network for each space to learn high-level domain-specific features directly from image data, allowing for discriminative or regression-based detection of anatomical objects without relying on handcrafted features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If handcrafted image features are used for anatomical object detection, then the detection method is computationally simpler, but the robustness and accuracy deteriorate for complex anatomical structures with large variations
Solution Approach 1:
The patent replaces handcrafted feature extraction methods with deep neural networks that automatically learn features from raw image data. The deep neural network substitutes the manual feature engineering process, enabling the system to automatically capture complex anatomical variations without requiring explicit feature design by humans.
Solution Approach 2:
The patent transforms the feature representation parameters from fixed handcrafted features to adaptive learned features. By changing the parameter space from predetermined feature descriptors to dynamic neural network activations, the system can adapt to various anatomical structures and their variations automatically.
2Measurement precision
If deep neural networks are used to learn high-level features directly from image data, 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 training one large deep neural network on the full parameter space, the approach divides the problem into smaller sub-problems, each handled by a separate neural network trained on a specific marginal space, reducing overall computational burden.
Solution Approach 2:
The patent performs preliminary feature learning in lower-dimensional marginal spaces before addressing the full parameter space. By pre-training neural networks on progressively larger marginal spaces, the system prepares intermediate representations that simplify the final detection task, reducing computational complexity of the complete system.
3Adaptability or versatility
If the full parameter space is used for training, then comprehensive detection capability is achieved, but training time and computational resources increase significantly
Solution Approach 1:
The patent divides the full parameter space into multiple marginal search spaces that can be trained independently and sequentially. This segmentation allows the training process to be distributed across multiple smaller tasks, significantly reducing the time and computational resources required compared to training on the complete parameter space at once.
Solution Approach 2:
The patent implements a staged training approach where neural networks are trained on marginal spaces of increasing dimensionality before the full parameter space. This preliminary action on smaller subspaces prepares the model progressively, reducing the overall training time while ensuring comprehensive coverage of the parameter space.
Data Source
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.