AI Agent for 3D Medical Image Object Localization
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Solution Overview
Problem
Conventional methods for object localization in 3D medical images face challenges due to variability in anatomy, image orientation, and field-of-view, with existing approaches being non-scalable, difficult to train, and limited in generalizing to various clinical cases.
Innovation Solution
A deep reinforcement-learning based AI agent navigates a nonlinear multi-dimensional parametric space to estimate parameters such as position, orientation, and scale of anatomical objects, using a Markov Decision Process and multi-scale progressive optimization to localize targets efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If atlas-based registration methods are used for object localization, then localization accuracy can be achieved, but the method requires complex non-rigid registration and is not scalable to large three-dimensional volumes
Solution Approach 1:
The patent segments the continuous non-linear parameter space into discrete bins along each dimension, transforming the complex continuous optimization problem into a series of simpler discrete classification problems. This segmentation allows the system to avoid complex non-rigid registration while maintaining localization accuracy through multi-resolution binning strategies.
Solution Approach 2:
The patent replaces the mechanical iterative optimization process of atlas-based registration with a data-driven classification approach using convolutional neural networks. The CNN automatically learns the mapping from image features to parameter space bins, substituting the complex mechanical registration process with a learned statistical model that scales better to large volumes.
2Adaptability or versatility
If regression-based methods are used for object localization, then the method can learn non-linear mapping from voxels to parameters, but such methods are difficult to train especially where the dataset has large variation in field of view
Solution Approach 1:
The patent transforms the regression problem into a classification problem by adding a discrete bin dimension to the parameter space. Instead of directly regressing continuous parameters from variable field-of-view images, the CNN classifies images into discrete bins along each parameter dimension. This dimensional transformation makes the problem more tractable and less sensitive to field-of-view variations during training.
Solution Approach 2:
The patent performs preliminary discretization of the parameter space into bins before the actual localization task. By pre-defining the bin structure and training the CNN to predict bin indices rather than continuous values, the system simplifies the learning task and reduces training difficulty, especially for datasets with large field-of-view variations.
3Productivity
If classification-based methods are used for object localization, then the method can perform localization by discretizing parametric space, but such methods impose dependencies in the parametric search space which can lead to suboptimal solutions
Solution Approach 1:
The patent segments the parameter space into multiple independent bins along each dimension, allowing the classification to proceed independently in each dimension. This segmentation reduces the dependencies between parameters during the classification process, enabling more efficient computation while maintaining the ability to capture non-linear relationships through the learned bin boundaries.
Solution Approach 2:
The patent employs a multi-resolution binning strategy where coarse bins are nested within finer bins. The CNN first classifies into coarse bins, then progressively refines the classification within smaller sub-bins. This nested structure allows the system to efficiently search the parametric space at multiple scales, achieving both computational efficiency and high localization accuracy without imposing strong dependencies between search steps.
4Measurement precision
If R-CNN based techniques are used for medical image analysis, then the method can perform object localization, but it requires very large annotated datasets to train and is difficult to generalize
Solution Approach 1:
The patent designs a universal classification framework that can handle multiple parameter dimensions (spatial coordinates, orientation angles, scale factors) simultaneously through a single CNN model. This multi-functional approach allows the system to generalize better across different anatomical structures and imaging conditions, reducing the need for large annotated datasets compared to specialized R-CNN approaches trained for specific tasks.
Data Source
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AI summary
System and methods are provided for localizing a target object in a medical image. The medical image is discretized into a plurality of images having different resolutions. For each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, a sequence of actions is performed for modifying parameters of a target object in the respective image. The parameters of the target object comprise nonlinear parameters of the target object. The sequence of actions is determined by an artificial intelligence agent trained for a resolution of the respective image to optimize a reward function. The target object is localized in the medical image based on the modified parameters of the target object in the last image.