The program will be available shortly. Please check back later.
Presenter: Luca Tomasetti, PHD student at Department of Electrical Engineering and Computer Science University of Stavanger
Abstract: Neurological disorders are the leading cause of disability adjusted life years and the second leading cause of death. Cerebral stroke is a major contributor to this burden, being the number one cause of neurological disability and the third-leading cause of death and disability combined. Computed tomography (CT)scan is a fast and widely used modality for early assessment in patients with symptoms of a cerebral stroke. CT perfusion (CTP) is often added to the protocol and is used by radiologists for assessing the severity of the stroke in the initial stages.
Precise and fast segmentation methods for ischemic areas (core and penumbra) in acute ischemic stroke patients are of significant clinical interest: a careful patient selection is essential to minimize complications and optimize costs associated with the procedure.
Current automatic segmentation methods for CTP mostly use already processed 3D color maps conventionally used for visual assessment by radiologists as input. We investigate different machine learning and deep learning methods that rely on various input to create precise segmentation architectures for ischemic areas.In this seminar, we show examples of our architectures and the results achieved by them.
In compliance with GDPR consent requirements, presentations given in a Visual Intelligence context may be recorded with the consent of the speaker. All recordings are edited to remove all faces, names and voices of other participants. Questions and comments by the audience will hence be removed and will not appear in the recording. With the freely given consent from the speaker, recorded presentation may be posted on the Visual Intelligence YouTube channel.