Automated segmentation utilizing fully convolutional networks
US10902598B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 25, 2018 |
| Grant date | Jan 26, 2021 |
| Priority date | — |
| Expiry date | Aug 22, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.