Automated intravascular plaque classification
First Claim
1. A non-transitory computer-readable storage device storing computer executable instructions that when executed by a computer control the computer to perform a method for intravascular plaque classification, the method comprising:
- accessing an intravascular optical coherence tomography (IVOCT) image of a region of tissue, where the IVOCT image includes a plurality of voxels, where the region of tissue includes a blood vessel;
generating a pre-processed image by pre-processing the IVOCT image, where generating the pre-processed image includes generating a blood vessel mask;
defining a moving box (mBox) based on the pre-processed image and the blood vessel mask, where the mBox defines a three-dimensional (3D) volume of interest (VOI) centered on a location in a view of the pre-processed image, where the mBox includes at least one member of the plurality of voxels, where a scanning range of the mBox is based, at least in part, on the blood vessel mask;
extracting a set of local features from the at least one member of the plurality of voxels;
providing the set of local features to a machine learning classifier, where the machine learning classifier includes a plurality of one-versus-the-rest (OVR) support vector machine (SVM)s, where the plurality of OVR SVMs includes an OVR-lipid (OVR-L) classifier, an OVR-calcium (OVR-C) classifier, and an OVR-fibrous (OVR-F) classifier, where a member of the plurality of OVR SVMs classifies a voxel based on a probability threshold and the set of local features, where the probability threshold is computed using a minimum squared distance (MSD) approach, or a Youden index approach;
receiving, from the machine learning classifier, a classification of the at least one member of the plurality of voxels;
generating an annotated image of the region of tissue based on the classification; and
controlling a percutaneous coronary intervention (PCI) apparatus to display the annotated image.
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Accused Products
Abstract
Methods and apparatus automatically classify intravascular plaque using features extracted from intravascular optical coherence tomography (IVOCT) imagery. One example apparatus includes an image acquisition circuit that accesses a set of IVOCT images, a pre-processing circuit that generates a blood vessel mask based on the IVOCT images, a feature extraction circuit that defines a three dimensional (3D) volume of interest centered on a location in a member of the set of IVOCT images, a classification circuit that generates a classification based on a probability that a voxel represents a type of plaque, and a visualization circuit that provides a visualization, substantially in real time, of a member of the set of IVOCT images and the classification, where the visualization includes a sector classification image, a labeled image, or a 3D visualization. A prognosis or treatment plan may be provided based on the visualization or the classification.
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Citations
17 Claims
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1. A non-transitory computer-readable storage device storing computer executable instructions that when executed by a computer control the computer to perform a method for intravascular plaque classification, the method comprising:
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accessing an intravascular optical coherence tomography (IVOCT) image of a region of tissue, where the IVOCT image includes a plurality of voxels, where the region of tissue includes a blood vessel; generating a pre-processed image by pre-processing the IVOCT image, where generating the pre-processed image includes generating a blood vessel mask; defining a moving box (mBox) based on the pre-processed image and the blood vessel mask, where the mBox defines a three-dimensional (3D) volume of interest (VOI) centered on a location in a view of the pre-processed image, where the mBox includes at least one member of the plurality of voxels, where a scanning range of the mBox is based, at least in part, on the blood vessel mask; extracting a set of local features from the at least one member of the plurality of voxels; providing the set of local features to a machine learning classifier, where the machine learning classifier includes a plurality of one-versus-the-rest (OVR) support vector machine (SVM)s, where the plurality of OVR SVMs includes an OVR-lipid (OVR-L) classifier, an OVR-calcium (OVR-C) classifier, and an OVR-fibrous (OVR-F) classifier, where a member of the plurality of OVR SVMs classifies a voxel based on a probability threshold and the set of local features, where the probability threshold is computed using a minimum squared distance (MSD) approach, or a Youden index approach; receiving, from the machine learning classifier, a classification of the at least one member of the plurality of voxels; generating an annotated image of the region of tissue based on the classification; and controlling a percutaneous coronary intervention (PCI) apparatus to display the annotated image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. An apparatus, comprising:
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a processor; a memory; an input/output interface; a set of circuits, where the set of circuits includes an image acquisition circuit, a pre-processing circuit, a feature extraction circuit, a classification circuit, and a visualization circuit; and an interface to connect the processor, the memory, the input/output interface and the set of circuits; where the image acquisition circuit accesses a set of intravascular optical coherence tomography (IVOCT) images of a region of tissue, where a member of the set of IVOCT images includes a plurality of voxels, where a member of the plurality of voxels includes a set of discriminative features, where the region of tissue includes a blood vessel; where the pre-processing circuit generates a blood vessel mask based on the set of IVOCT images, where the pre-processing circuit provides the blood vessel mask to the feature extraction circuit; where the feature extraction circuit defines a moving box (mBox), where the mBox defines a three dimensional (3D) volume of interest centered on a location in the member of the set of IVOCT images, where the mBox includes at least one member of the plurality of voxels, where the mBox is defined by a set of dimensions and the blood vessel mask, where the feature extraction circuit extracts the set of discriminative features from the at least one member of the plurality of voxels, and where the feature extraction circuit provides the set of discriminative features to the classification circuit; where the classification circuit generates a classification of the voxel as a type of plaque based on a probability that the voxel represents the type of plaque, where the probability is based, at least in part, on the set of discriminative features, where the classification circuit includes a plurality of one-versus-the-rest (OVR) support vector machines (SVM)s, where a first member of the plurality of OVR SVMs is an OVR-lipid (OVR-L) classifier, where a second, different member of the plurality of OVR SVMs is an OVR-calcium (OVR-C) classifier, and where a third, different member of the plurality of OVR SVMs is an OVR-fibrous (OVR-F) classifier, where a member of the plurality of OVR SVMs classifies a voxel based on a probability threshold and the set of discriminative features, where the classification circuit computes the probability threshold using a minimum squared distance (MSD) approach, or a Youden index approach, and where the OVR-L classifier, the OVR-C classifier, or the OVR-F classifier employs a regularization parameter and a radial basis function (RDF) Gaussian kernel; and where the visualization circuit generates a visualization of a member of the set of IVOCT images and the classification, where the visualization includes a sector classification image, a labeled image, or a 3D visualization, and where the visualization circuit provides the visualization to a display, substantially in real time. - View Dependent Claims (14, 15, 16)
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17. A non-transitory computer-readable storage device storing computer executable instructions that when executed by a computer control the computer to perform a method, the method comprising:
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accessing a set of intravascular optical coherence tomography (IVOCT) images of a region of tissue demonstrating intravascular plaque, where the set of IVOCT images is acquired from a patient during a percutaneous coronary intervention (PCI), where a member of the set of IVOCT images includes at least one voxel, where the region of tissue includes a blood vessel; generating a blood vessel mask by preprocessing the set of IVOCT images; defining a volume of interest (VOI) based on the set of IVOCT images and the blood vessel mask, where the VOI includes a member of the at least one voxels; extracting a set of discriminatory features from the member of the at least one voxels, where the set of discriminatory features includes an optical feature, an intensity variation feature, or a spatial texture feature; providing the set of discriminatory features to a plurality of support vector machines (SVMs), where the plurality of support vector machines includes a one-versus-rest lipid (OVR-L) SVM, an OVR-fibrous (OVR-F) SVM, and an OVR-calcium (OVR-C) SVM; receiving, from the plurality of SVMs, a classification of the voxel; generating a visualization of the blood vessel based on the IVOCT and the classification, where the visualization includes a sector classification, a labeled image, or a 3D visualization; displaying the visualization; and generating a personalized treatment plan for the patient, based, at least in part, on the visualization and the classification.
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Specification