Brain neural imaging has become a very important tool in the diagnosis and treatment of cerebrovascular diseases. The computerized automation of brain neural imaging-based analysis and diagnosis is the main trend of current development. This integrated project is aimed at constructing an automated brain neural imaging analysis and diagnosis system. The core capability of this system includes automated segmentation of ischemia, infarct, and ischemic penumbra of acute ischemic stroke, as well as automated segmentation of white matter hyperintensity. A graphic user interface will be provided to facilitate friendly execution and result display. A key management platform will be built to be able to control the remote execution right of the core program functions when a promotion or sharing of the core program with extramural users is needed. This integrated project includes two subprojects. Subproject 1 will be responsible for the development of the automated cerebral lesion segmentation algorithms. A rule-base algorithm will be developed for the segmentation of acute stroke infarct and ischemia, while an artificial intelligence-based algorithm will be developed for the segmentation of white matter hyperintensity. Subproject 2 will be in charge of the design and construction of the graphic user interface and the key management platform.
|Effective start/end date||1/06/22 → 31/07/23|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Lesion segmentation
- Key management system
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.