3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning
US11676281B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 20, 2021 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Sep 24, 2041 |
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. The segmentation neural network can include a sequence of multiple encoder blocks and a decoder subnetwork. Training the segmentation neural network can include determining a set of error values for a segmentation channel; identifying the highest error values from the set of error values for the segmentation channel; and determining a segmentation loss based on the highest error values identified for the segmentation channel.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.