Automated cardiac volume segmentation
US10871536B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 29, 2016 |
| Grant date | Dec 22, 2020 |
| Priority date | — |
| Expiry date | Mar 28, 2037 |
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, such as the human heart. The systems and methods employ convolutional neural networks (CNNs) to autonomously segment various parts of an anatomical structure represented by image data, such as 3D MRI data. The convolutional neural network utilizes two paths, a contracting path which includes convolution/pooling layers, and an expanding path which includes upsampling/convolution layers. The loss function used to validate the CNN model may specifically account for missing data, which allows for use of a larger training set. The CNN model may utilize multi-dimensional kernels (e.g., 2D, 3D, 4D, 6D), and may include various channels which encode spatial data, time data, flow data, etc. The systems and methods of the present disclosure also utilize CNNs to provide automated detection and display of landmarks in images of anatomical structures.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.