Co-heterogeneous and adaptive 3D pathological abdominal organ segmentation using multi-source and multi-phase clinical image datasets
US11568174B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 4, 2020 |
| Grant date | Jan 31, 2023 |
| Priority date | — |
| Expiry date | Jun 1, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/031
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure describes a computer-implemented method for processing clinical three-dimensional image. The method includes training a fully supervised segmentation model using a labelled image dataset containing images for a disease at a predefined set of contrast phases or modalities, allow the segmentation model to segment images at the predefined set of contrast phases or modalities; finetuning the fully supervised segmentation model through co-heterogenous training and adversarial domain adaptation (ADA) using an unlabelled image dataset containing clinical multi-phase or multi-modality image data, to allow the segmentation model to segment images at contrast phases or modalities other than the predefined set of contrast phases or modalities; and further finetuning the fully supervised segmentation model using domain-specific pseudo labelling to identify pathological regions missed by the segmentation model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.