3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning
US11100647B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 9, 2019 |
| Grant date | Aug 24, 2021 |
| Priority date | — |
| Expiry date | Sep 9, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30196
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output, wherein the segmentation neural network comprises a sequence of multiple encoder blocks, wherein: each encoder block is a residual neural network block comprising one or more two-dimensional convolutional neural network layers, one or more three-dimensional convolutional neural network layers, or both, and each encoder block is configured to process a respective encoder block input to generate a respective encoder block output wherein a spatial resolution of the encoder block output is lower than a spatial resolution of the encoder block input.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.