Automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network
US11562491B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 3, 2021 |
| Grant date | Jan 24, 2023 |
| Priority date | — |
| Expiry date | Dec 3, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention discloses an automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network. Under a coarse-to-fine two-step segmentation framework, the method uses a densely connected dilated convolutional neural network as a basis network architecture to obtain multi-scale image feature expression of the target. An initial segmentation probability map of the pancreas is predicted in the coarse segmentation stage. A saliency map is then calculated through saliency transformation based on a geodesic distance transformation. A saliency-aware module is introduced into the feature extraction layer of the densely connected dilated convolutional neural network, and the saliency-aware densely connected dilated convolutional neural network is constructed as the fine segmentation network model. A coarse segmentation model and the fine segmentation model are trained using a training set, respectively.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.