Computed tomography image processing and data analysis
AssigneeAbbyy Production LLC
InventorsKonstantin Zuyev, Aleksey Matyushin, Stepan Lobastov
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Abstract
Systems and methods for computed tomography image processing and data analysis. An example image may comprise: receiving a plurality of input images of a scanned body; processing, by a first convolutional neural network, the plurality of input images to produce one or more pseudo radioisotope emission (pseudo-RE) images of the scanned body; and processing, by a second convolutional neural network, the plurality of pseudo-RE images to classify one or more input images of the plurality of input images with respect to a specified medical diagnosis with respect to the scanned body.
Claims22
A method comprising:
- receiving a plurality of input images of a scanned body;
- processing, by a first convolutional neural network, the plurality of input images to produce one or more pseudo radioisotope emission (pseudo-RE) images of the scanned body, wherein the pseudo-RE images emulate radioactive tracer distribution within the scanned body at a cellular level;
- processing, by a second convolutional neural network, the plurality of pseudo-RE images to classify one or more input images of the plurality of input images with respect to a specified medical diagnosis with respect to the scanned body;
- and
- determining an error margin associated with classifying one or more input images, wherein the error margin reflects a ratio of true positive and false positive outcomes.
A system, comprising:
- a memory;
- a processor, coupled to the memory, the processor configured to:
- receive a plurality of input images of a scanned body;
- process, by a first convolutional neural network, a plurality of input images of a scanned body to produce one or more pseudo radioisotope emission (pseudo-RE) images of the scanned body, wherein the pseudo-RE images emulate radioactive tracer distribution within the scanned body at a cellular level;
- process, by a second convolutional neural network, the plurality of pseudo-RE images to classify one or more input images of the plurality of input images with respect to a specified medical diagnosis with respect to the scanned body;
- and
- determine an error margin associated with classifying one or more input images, wherein the error margin reflects a ratio of true positive and false positive outcomes.
A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computing device, cause the computing device to perform operations comprising:
- receiving a plurality of input images of a scanned body;
- processing, by a first convolutional neural network, a plurality of input images of a scanned body to produce one or more pseudo radioisotope emission (pseudo-RE) images of the scanned body, wherein the pseudo-RE images emulate radioactive tracer distribution within the scanned body at a cellular level;
- and
- processing, by a second convolutional neural network, the plurality of pseudo-RE images to yield a probability of one or more input images of the plurality of input images comprising diagnostically-significant image features associated with a specified medical diagnosis with respect to the scanned body, wherein the probability of the input images comprising the diagnostically-significant image features is determined by a cross-entropy function, and wherein an error signal of the cross-entropy function network is proportional to a difference between expected and actual output values.
Dependencies are inferred from the claim text.