Uncertainty-aware deep reinforcement learning for anatomical landmark detection in medical images
US12039728B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 18, 2022 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Jan 30, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described are techniques for uncertainty-aware anatomical landmark detection, using, for example, a deep reinforcement learning (DRL) anatomical landmark detection agent. For instance, a process can include generating one or more image features for an input medical image using a first sub-network of the anatomical landmark detection agent. A softmax layer of a second sub-network of the anatomical landmark detection agent can generate a plurality of discrete Q-value distributions for a set of allowable actions associated with movement of the agent within the medical image. An anatomical landmark location within the medical image can be predicted using the discrete Q-value distributions. An uncertainty can be determined for the predicted anatomical landmark location, based on an average full width half maximum (FWHM) calculated for the plurality of discrete Q-value distributions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.