Medical image segmentation from raw data using a deep attention neural network
US10922816B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 9, 2019 |
| Grant date | Feb 16, 2021 |
| Priority date | — |
| Expiry date | Aug 29, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various approaches provide improved segmentation from raw data. Training samples are generated by medical imaging simulation from digital phantoms. These training samples provide raw measurements, which are used to learn to segment. The segmentation task is the focus, so image reconstruction loss is not used. Instead, an attention network is used to focus the training and trained network on segmentation. Recurrent segmentation from the raw measurements is used to refine the segmented output. These approaches may be used alone or in combination, providing for segmentation from raw measurements with less influence of noise or artifacts resulting from a focus on reconstruction.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.