Invention Grant
- Patent Title: Synthetically generating medical images using deep convolutional generative adversarial networks
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Application No.: US17214442Application Date: 2021-03-26
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Publication No.: US11990224B2Publication Date: 2024-05-21
- Inventor: Hamid Jafarkhani , Saeed Karimi-Bidhendi , Arash Kheradvar
- Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Applicant Address: US CA Oakland
- Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee Address: US CA Oakland
- Agency: Perkins Coie LLP
- Main IPC: G06T7/143
- IPC: G06T7/143 ; G06F18/21 ; G06F18/214 ; G06N3/08 ; G06N7/01 ; G06T7/00 ; G06V10/82 ; G16H30/40

Abstract:
Methods, devices, and systems that are related to facilitating an automated, fast and accurate model for cardiac image segmentation, particularly for image data of children with complex congenital heart disease are disclosed. In one example aspect, a generative adversarial network is disclosed. The generative adversarial network includes a generator configured to generate synthetic imaging samples associated with a cardiovascular system, and a discriminator configured to receive the synthetic imaging samples from the generator and determine probabilities indicating likelihood of the synthetic imaging samples corresponding to real cardiovascular imaging sample. The discriminator is further configured to provide the probabilities determined by the discriminator to the generator and the discriminator to allow the parameters of the generator and the parameters of the discriminator to be adjusted iteratively until an equilibrium between the generator and the discriminator is established.
Public/Granted literature
- US20210312242A1 Synthetically Generating Medical Images Using Deep Convolutional Generative Adversarial Networks Public/Granted day:2021-10-07
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