Method and system for intracerebral hemorrhage detection and segmentation based on a multi-task fully convolutional network
US11170504B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 28, 2020 |
| Grant date | Nov 9, 2021 |
| Priority date | — |
| Expiry date | May 14, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/031
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the disclosure provide systems and methods for detecting an intracerebral hemorrhage (ICH). The system includes a communication interface configured to receive a sequence of image slices and an end-to-end multi-task learning model. The sequence of image slices is the head scan images of a subject acquired by an image acquisition device. The end-to-end multi-task learning model includes an encoder, a bi-directional Convolutional Recurrent Neural Network (ConvRNN), a decoder, and a classifier. The system further includes at least one processor configured to extract feature maps from each image slice using the encoder, capture contextual information between adjacent image slices using the bi-directional ConvRNN, and detect the ICH of the subject using the classifier based on the extracted feature maps of the image slices and the contextual information or segment each image slice using the decoder to obtain an ICH region based on the extracted feature maps of the image slice.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.