3D quantitative analysis with deep learning
US11302006B2 · kind B2 · utility
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Key dates
| Filing date | Nov 24, 2020 |
| Grant date | Apr 12, 2022 |
| Priority date | — |
| Expiry date | Nov 24, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30041
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning model is trained to identify the texture difference between the different layers of a multilayer object. By training with data in full 3D space, the resulting model is capable of predicting the probability that each pixel in a 3D image belongs to a certain layer. With the resulting probability map, comparing probabilities allows one to determine boundaries between layers, and/or other properties and useful information such as volume data.
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