Medical scan image analysis system
First Claim
1. A medical scan image analysis system, comprising:
- a processing system that includes a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising;
receiving a plurality of medical scans via a network, wherein each of the plurality of medical scans represents a three-dimensional anatomical region and includes a plurality of cross-sectional image slices;
generating a plurality of three-dimensional subregions corresponding to each of the plurality of medical scans by selecting a proper subset of the plurality of cross-sectional image slices from each of the plurality of medical scans, and by further selecting a two-dimensional subregion from each proper subset of the plurality of cross-sectional image slices of the each of the medical scans;
performing a learning algorithm on the plurality of three-dimensional subregions to generate a fully convolutional neural network;
receiving a new medical scan that is not included in the plurality of medical scans;
generating inference data corresponding to the new medical scan by performing a first inference algorithm on the new medical scan by utilizing the fully convolutional neural network; and
identifying an inferred abnormality in the new medical scan based on the inference data;
wherein performing the learning algorithm includes utilizing a forward propagation algorithm on the plurality of three-dimensional subregions to generate a preliminary set of neural network parameters, and by utilizing a back propagation algorithm to generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters, and wherein performing the first inference algorithm includes utilizing the forward propagation algorithm on the new medical scan based on the updated set of neural network parameters.
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Abstract
A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.
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Citations
18 Claims
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1. A medical scan image analysis system, comprising:
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a processing system that includes a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising; receiving a plurality of medical scans via a network, wherein each of the plurality of medical scans represents a three-dimensional anatomical region and includes a plurality of cross-sectional image slices; generating a plurality of three-dimensional subregions corresponding to each of the plurality of medical scans by selecting a proper subset of the plurality of cross-sectional image slices from each of the plurality of medical scans, and by further selecting a two-dimensional subregion from each proper subset of the plurality of cross-sectional image slices of the each of the medical scans; performing a learning algorithm on the plurality of three-dimensional subregions to generate a fully convolutional neural network; receiving a new medical scan that is not included in the plurality of medical scans; generating inference data corresponding to the new medical scan by performing a first inference algorithm on the new medical scan by utilizing the fully convolutional neural network; and identifying an inferred abnormality in the new medical scan based on the inference data; wherein performing the learning algorithm includes utilizing a forward propagation algorithm on the plurality of three-dimensional subregions to generate a preliminary set of neural network parameters, and by utilizing a back propagation algorithm to generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters, and wherein performing the first inference algorithm includes utilizing the forward propagation algorithm on the new medical scan based on the updated set of neural network parameters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method for execution by a medical scan image analysis system that includes a processor, the method comprising:
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receiving a plurality of medical scans via a network, wherein each of the plurality of medical scans represents a three-dimensional anatomical region and includes a plurality of cross-sectional image slices; determining a density window for the plurality of medical scans by determining a low end density cut-off value and high end density cut-off value based on Hounsfield units of the each of the plurality of medical scans; generating a plurality of preprocessed medical scans by utilizing the density window to mask a first subset of pixels of the plurality of medical scans; generating a plurality of three-dimensional subregions corresponding to each of the plurality of medical scans by selecting a proper subset of the plurality of cross-sectional image slices from each of the plurality of medical scans, and by further selecting a two-dimensional subregion from each proper subset of the plurality of cross-sectional image slices of the each of the medical scans, wherein the plurality of three-dimensional subregions are generated by utilizing the plurality of preprocessed medical scans; performing a learning algorithm on the plurality of three-dimensional subregions to generate a fully convolutional neural network; receiving a new medical scan that is not included in the plurality of medical scans; generating inference data corresponding to the new medical scan by performing a first inference algorithm on the new medical scan by utilizing the fully convolutional neural network; and identifying an inferred abnormality in the new medical scan based on the inference data. - View Dependent Claims (13, 14, 15, 16, 17)
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18. An article of manufacture that includes a tangible storage medium that stores operational instructions, that when executed by a processor, causes the processor to:
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receive a plurality of medical scans via a network, wherein each of the plurality of medical scans represents a three-dimensional anatomical region and includes a plurality of cross-sectional image slices; generate a plurality of three-dimensional subregions corresponding to each of the plurality of medical scans by selecting a proper subset of the plurality of cross-sectional image slices from each of the plurality of medical scans, and by further selecting a two-dimensional subregion from each proper subset of the plurality of cross-sectional image slices of the each of the medical scans; perform a learning algorithm on the plurality of three-dimensional subregions to generate a fully convolutional neural network; receive a new medical scan that is not included in the plurality of medical scans; generate inference data corresponding to the new medical scan by performing a first inference algorithm on the new medical scan by utilizing the fully convolutional neural network; and identify an inferred abnormality in the new medical scan based on the inference data; wherein performing the learning algorithm includes utilizing a forward propagation algorithm on the plurality of three-dimensional subregions to generate a preliminary set of neural network parameters, and by utilizing a back propagation algorithm to generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters, and wherein performing the first inference algorithm includes utilizing the forward propagation algorithm on the new medical scan based on the updated set of neural network parameters.
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Specification