3D quantitative analysis of retinal layers with deep learning
US10878574B2 · kind B2 · utility
3Cited by
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Key dates
| Filing date | Feb 15, 2019 |
| Grant date | Dec 29, 2020 |
| Priority date | — |
| Expiry date | Jul 5, 2039 |
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.