Multi-scale deep reinforcement machine learning for N-dimensional segmentation in medical imaging
First Claim
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1. A method for three-dimensional segmentation based on machine learning in a medical imaging system, the method comprising:
- scanning, by a medical scanner, a patient, the medical scanner comprising a magnetic resonance, computed tomography, x-ray, or ultrasound imaging system;
loading, from memory, a medical dataset representing a three-dimensional region of the patient, the medical dataset being from the scanning by the medical scanner, the medical dataset comprising magnetic resonance, computed tomography, x-ray, or ultrasound data;
applying, by a machine, the medical dataset to a multi-scale deep reinforcement machine-learnt model, the multi-scale deep reinforcement machine-learned model trained with multi-scale deep reinforcement learning to segment boundaries of a three-dimensional object from the medical dataset, the multi-scale deep reinforcement machine-learnt model including a machine-learnt policy of a sequence of actions for shape evolution over iterative refinements of the boundaries of the object, the machine-learnt policy trained to select each of the actions from possibilities given a state of fitting at each of the iterative refinements, the actions including actions in different resolutions of the medical dataset, the sequence resulting from the machine-learnt policy training based on maximizing rewards, the boundaries and refinements described using statistical shape-modeling, front propagation modeling, or voxel mask modeling, the multi-scale deep reinforcement machine-learnt model trained with natural evolution strategies to explore a parameter space based on the statistical shape-modeling, front propagation modeling, or voxel mask modeling;
rendering, by a renderer, an image of the three-dimensional object based on the boundaries determined with the policy; and
displaying the image on a display device.
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Abstract
Multi-scale deep reinforcement learning generates a multi-scale deep reinforcement model for multi-dimensional (e.g., 3D) segmentation of an object. In this context, segmentation is formulated as learning an image-driven policy for shape evolution that converges to the object boundary. The segmentation is treated as a reinforcement learning problem, and scale-space theory is used to enable robust and efficient multi-scale shape estimation. By learning an iterative strategy to find the segmentation, the learning challenges of end-to-end regression systems may be addressed.
36 Citations
20 Claims
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1. A method for three-dimensional segmentation based on machine learning in a medical imaging system, the method comprising:
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scanning, by a medical scanner, a patient, the medical scanner comprising a magnetic resonance, computed tomography, x-ray, or ultrasound imaging system; loading, from memory, a medical dataset representing a three-dimensional region of the patient, the medical dataset being from the scanning by the medical scanner, the medical dataset comprising magnetic resonance, computed tomography, x-ray, or ultrasound data; applying, by a machine, the medical dataset to a multi-scale deep reinforcement machine-learnt model, the multi-scale deep reinforcement machine-learned model trained with multi-scale deep reinforcement learning to segment boundaries of a three-dimensional object from the medical dataset, the multi-scale deep reinforcement machine-learnt model including a machine-learnt policy of a sequence of actions for shape evolution over iterative refinements of the boundaries of the object, the machine-learnt policy trained to select each of the actions from possibilities given a state of fitting at each of the iterative refinements, the actions including actions in different resolutions of the medical dataset, the sequence resulting from the machine-learnt policy training based on maximizing rewards, the boundaries and refinements described using statistical shape-modeling, front propagation modeling, or voxel mask modeling, the multi-scale deep reinforcement machine-learnt model trained with natural evolution strategies to explore a parameter space based on the statistical shape-modeling, front propagation modeling, or voxel mask modeling; rendering, by a renderer, an image of the three-dimensional object based on the boundaries determined with the policy; and displaying the image on a display device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for multi-dimensional segmentation based on machine learning in a medical imaging system, the method comprising:
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scanning, by a medical scanner, a patient, the medical scanner comprising a magnetic resonance, computed tomography, x-ray, or ultrasound imaging system; loading, from memory, a medical dataset representing a three-dimensional region of the patient, the medical dataset being from the scanning by the medical scanner, the medical dataset comprising magnetic resonance, computed tomography, x-ray, or ultrasound data; applying, by a machine, the medical dataset to a multi-scale, deep reinforcement machine-learnt model, the multi-scale, deep reinforcement machine-learned model trained with multi-scale, deep reinforcement learning to segment boundaries of a multi-dimensional object from the medical dataset, the multi-scale, deep reinforcement machine-learnt model including a machine-learnt policy of a sequence of actions for shape evolution over iterative refinements of the boundaries in scale at different resolutions of the medical dataset and in location, the multi-scale deep reinforcement machine-learnt model trained with natural evolution strategies to explore a parameter space; and generating, by a graphics processor on a display device, an image of the multi-dimensional object based on the boundaries determined with the policy. - View Dependent Claims (16, 17, 18, 19)
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20. A method for three-dimensional segmentation based on machine learning in a medical imaging system, the method comprising:
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scanning, by a medical scanner, a patient, the medical scanner comprising a magnetic resonance, computed tomography, x-ray, or ultrasound imaging system; loading, from memory, a medical dataset representing a three-dimensional region of the patient, the medical dataset being from the scanning by the medical scanner, the medical dataset comprising magnetic resonance, computed tomography, x-ray, or ultrasound data; applying, by a machine, the medical dataset to a multi-scale deep reinforcement machine-learnt model, the multi-scale deep reinforcement machine-learned model trained with multi-scale deep reinforcement learning to segment boundaries of a three-dimensional object from the medical dataset, the multi-scale deep reinforcement machine-learnt model including a machine-learnt policy of a sequence of actions for shape evolution over iterative refinements of the boundaries of the object, including actions in different resolutions of the medical dataset, the boundaries and refinements described using front propagation modeling, or voxel mask modeling, wherein applying comprises one of; (a) applying with the boundaries and refinements described using the front propagation modeling and wherein the shape evolution comprises change in a speed of particles in a hypersurface of the front propagation modeling, or (b) applying with the boundaries and refinements described using the voxel mask modeling and wherein the shape evolution comprises change in state per voxel of the medical dataset of the voxel mask modeling; rendering, by a renderer, an image of the three-dimensional object based on the boundaries determined with the policy; and displaying the image on a display device.
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Specification