Patient risk stratification based on body composition derived from computed tomography images using machine learning
US11322259B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 10, 2018 |
| Grant date | May 3, 2022 |
| Priority date | — |
| Expiry date | Sep 10, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30096
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
A system and method for determining patient risk stratification is provided based on body composition derived from computed tomography images using segmentation with machine learning. The system may enable real-time segmentation for facilitating clinical application of body morphological analysis sets. A fully-automated deep learning system may be used for the segmentation of skeletal muscle cross sectional area (CSA). Whole-body volumetric analysis may also be performed. The fully-automated deep segmentation model may be derived from an extended implementation of a Fully Convolutional Network with weight initialization of a pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.