Generating simulated body parts for images
AssigneeInternational Business Machines Corporation
InventorsSun Young Park, Dusty Sargent
Life of this patent
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Abstract
A system for generating simulated body parts for images may include a body part recognition convolutional neural network (CNN) to recognize a body part in an input image. The body part recognition CNN may be trained using first training data including training images including body parts contained in the input image being identified. The system may also include a body part generative adversarial network (GAN) to complete an image of the body part in the input image based on a body part identification output by the body part recognition CNN. The body part GAN may be trained using second training data including at least partial training images.
Claims18
A system for generating simulated body parts for images, comprising:
- a body part recognition convolutional neural network (CNN) to recognize a body part in an input image, the body part recognition CNN being trained using first training data comprising training images comprising body parts contained in the input image being identified;
- a body part sequence recurrent neural network (RNN) to refine identification of the body part output by the body part recognition CNN by processing a series of images output by the body part recognition CNN in sequence;
- and
- a body part generative adversarial network (GAN) to complete an image of the body part in the input image by including body parts missing from the input image based on the identification of the body part output by the body part recognition CNN, the body part GAN being trained using second training data comprising at least partial training images.
A method of augmenting a computed tomography (CT) image comprising:
- training a body part recognition convolutional neural network (CNN) to identify a body part in an input CT image using first training data comprising CT images comprising body parts contained in the input CT image being identified;
- with a body part sequence recurrent neural network (RNN), processing a series of images output by the body part recognition CNN, in sequence to refine the identification of the body part output by the body part recognition CNN;
- training a first generative adversarial network (GAN) to complete an image of the body part in the input CT image based on body part identification output by the trained body part recognition CNN using training data comprising partial and complete CT images.
A computer program product for augmenting a computed tomography (CT) image, the computer program product comprising:
- a computer readable storage medium comprising computer usable program code embodied therewith, the computer usable program code to, when executed by a processor:
- recognize, with a body part recognition convolutional, neural network (CNN) trained using first training data comprising training images with body parts contained in an input CT image being identified, at least one body part in the input CT image, the input CT image comprising a partially captured image of a portion of the body comprising the at least one body part;
- refine identification of the body part output by the body part recognition CNN with a body part sequence recurrent neural network (RNN) by processing a series of images output by the body part recognition CNN in sequence;
- and
- complete the input CT image based on the recognition of body parts shown to generate a complete CT image by including, for body parts missing from the input CT image, images of body parts captured from previous patients.
Dependencies are inferred from the claim text.