Medical image segmentation method based on U-Net
US11580646B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 5, 2022 |
| Grant date | Feb 14, 2023 |
| Priority date | — |
| Expiry date | Jan 5, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30101
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A medical image segmentation method based on a U-Net, including: sending real segmentation image and original image to a generative adversarial network for data enhancement to generate a composite image with a label; then putting the composite image into original data set to obtain an expanded data set, and sending the expanded data set to improved multi-feature fusion segmentation network for training. A Dilated Convolution Module is added between the shallow and deep feature skip connections of the segmentation network to obtain receptive fields with different sizes, which enhances the fusion of detail information and deep semantics, improves the adaptability to the size of the segmentation target, and improves the medical image segmentation accuracy. The over-fitting problem that occurs when training the segmentation network is alleviated by using the expanded data set of the generative adversarial network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.