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US 12,705,704 B1Granted
Synthetic augmentation based autoencoding neural network for speckle removal in intracardiac ultrasound imaging
AssigneeyoR Labs, Inc.
InventorAnshumali Roy
Life of this patent
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Abstract
The systems and methods generate synthetic speckle data. The system introduces speckle noise into a clean dataset of images. The system utilizes a machine learning model, such as a model with a convolutional autoencoder architecture, and trains the machine learning model on the synthetic training data. The system uses the trained machine learning model on intracardiac echocardiography images to remove speckle substantially in real-time.
Claims20
A system comprising:
- one or more non-transitory data storage media;
- and
- one or more first computer hardware processors in communication with the one or more non-transitory data storage media, wherein the one or more first computer hardware processors are configured to execute first computer-executable instructions to at least:
- receive a set of tissue images;
- determine, from the set of tissue images, a set of synthetic speckled tissue images, wherein to determine the set of synthetic speckled tissue images comprises:
- create noise data;
- modify the noise data with at least a filter that results in speckle filter data;
- apply, to a first tissue image from the set of tissue images, the speckle filter data as an overlay that results in a first intermediate synthetic speckled tissue image;
- and
- transform the first intermediate synthetic speckled tissue image into a first synthetic speckled tissue image in a sector format, wherein the set of synthetic speckled tissue images comprises the first synthetic speckled tissue image;
- determine, from the set of tissue images, a set of images as targets;
- create a training set comprising (i) the set of synthetic speckled tissue images and (ii) the set of images as targets;
- and
- train a convolutional autoencoder with the training set.
11 more elements
A computer-implemented method comprising:
- receiving a set of initial training images;
- determining, from the set of initial training images, a set of synthetic speckled images, wherein determining the set of synthetic speckled images comprises:
- creating noise data;
- modifying the noise data with at least a filter that results in speckle filter data;
- and
- applying, to a first training image from the set of initial training images, the speckle filter data as an overlay that results in a first synthetic speckled image, wherein the set of synthetic speckled images is determined from the first synthetic speckled image;
- determining, from the set of initial training images, a set of images as targets;
- creating a training set comprising (i) the set of synthetic speckled images and (ii) the set of images as targets;
- and
- training a machine learning model with the training set.
7 more elements
A system comprising:
- one or more non-transitory data storage media;
- and
- one or more first computer hardware processors in communication with the one or more non-transitory data storage media, wherein the one or more first computer hardware processors are configured to execute first computer-executable instructions to at least:
- receive a set of initial training images;
- determine, from the set of initial training images, a set of synthetic speckled images, wherein to determine the set of synthetic speckled images comprises:
- create noise data;
- modify the noise data with at least a filter that results in speckle filter data;
- and
- apply, to a first training image from the set of initial training images, the speckle filter data as an overlay that results in a first synthetic speckled image, wherein the set of synthetic speckled images is determined from the first synthetic speckled image;
- determine, from the set of initial training images, a set of images as targets;
- create a training set comprising (i) the set of synthetic speckled images and (ii) the set of images as targets;
- and
- train a machine learning model with the training set.
10 more elements
Dependencies are inferred from the claim text.