MULTI-SCALE DEEP REINFORCEMENT MACHINE LEARNING FOR N-DIMENSIONAL SEGMENTATION IN MEDICAL IMAGING
First Claim
1. A method for three-dimensional segmentation based on machine learning in a medical imaging system, the method comprising:
- scanning, by a magnetic resonance, computed tomography, x-ray, or ultrasound imaging system, a patient, the scanning providing a medical dataset representing a three-dimensional region of a patient, 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 three-dimensional 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 of shape representation parameters, the sequence resulting from the machine-learnt policy training based on rewards, and the boundaries and refinements described using the shape representation parameters;
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.
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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 magnetic resonance, computed tomography, x-ray, or ultrasound imaging system, a patient, the scanning providing a medical dataset representing a three-dimensional region of a patient, 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 three-dimensional 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 of shape representation parameters, the sequence resulting from the machine-learnt policy training based on rewards, and the boundaries and refinements described using the shape representation parameters; 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, 15)
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16. 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 magnetic resonance, computed tomography, x-ray, or ultrasound imaging system, a patient, the scanning providing a medical dataset representing a three-dimensional region of a patient, 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 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 to explore a parameter space; and generating, by a graphics processor, an image of the multi-dimensional object based on the boundaries determined with the policy. - View Dependent Claims (17, 18, 19, 20)
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Specification