Image segmentation and object detection using fully convolutional neural network
US10304193B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 17, 2018 |
| Grant date | May 28, 2019 |
| Priority date | — |
| Expiry date | Aug 17, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/031
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to digital image segmentation, region of interest identification, and object recognition. This disclosure describes a method, a system, for image segmentation based on fully convolutional neural network including an expansion neural network and contraction neural network. The various convolutional and deconvolution layers of the neural networks are architected to include a coarse-to-fine residual learning module and learning paths, as well as a dense convolution module to extract auto context features and to facilitate fast, efficient, and accurate training of the neural networks capable of producing prediction masks of regions of interest. While the disclosed method and system are applicable for general image segmentation and object detection/identification, they are particularly suitable for organ, tissue, and lesion segmentation and detection in medical images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.