Automatic liver segmentation using adversarial image-to-image network
US10600185B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 23, 2018 |
| Grant date | Mar 24, 2020 |
| Priority date | — |
| Expiry date | Jul 6, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30056
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus for automated liver segmentation in a 3D medical image of a patient is disclosed. A 3D medical image, such as a 3D computed tomography (CT) volume, of a patient is received. The 3D medical image of the patient is input to a trained deep image-to-image network. The trained deep image-to-image network is trained in an adversarial network together with a discriminative network that distinguishes between predicted liver segmentation masks generated by the deep image-to-image network from input training volumes and ground truth liver segmentation masks. A liver segmentation mask defining a segmented liver region in the 3D medical image of the patient is generated using the trained deep image-to-image network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.