Structure correcting adversarial network for chest X-rays organ segmentation
US10699412B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 20, 2018 |
| Grant date | Jun 30, 2020 |
| Priority date | — |
| Expiry date | Sep 26, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/031
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Organ segmentation in chest X-rays using convolutional neural networks is disclosed. One embodiment provides a method to train a convolutional segmentation network with chest X-ray images to generate pixel-level predictions of target classes. Another embodiment will also train a critic network with an input mask, wherein the input mask is one of a segmentation network mask and a ground truth annotation, and outputting a probability that the input mask is the ground truth annotation instead of the prediction by the segmentation network, and to provide the probability output by the critic network to the segmentation network to guide the segmentation network to generate masks more consistent with learned higher-order structures.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.