Weakly supervised probabilistic atlas generation through multi-atlas label fusion
US10169873B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 23, 2017 |
| Grant date | Jan 1, 2019 |
| Priority date | — |
| Expiry date | Mar 23, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30101
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In many medical image classification problems, distinctive image features are often localized in certain anatomical regions. The key to efficient and accurate classification in such problems is the localization of the region of interest (ROI). To address this problem, a multi-atlas label fusion technique was developed for automatic ROI detection. Given training images with class labels, the present method infers voxel-wise scores for each image showing how distinctive each voxel is for categorizing the image. The present method for ROI segmentation and for class specific ROI patch extraction in a 2D cardiac CT body part classification application was applied and shows the effectiveness of the detected ROIs.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.