A Multi-task Deep learning Model for Simultaneous Segmentations of Penumbra and Infarct in Patients with Acute Ischemic Stroke

Motivation: Arterial spin labeling (ASL) has shown comparable results with dynamic susceptibility contrast magnetic resonance imaging in evaluating hypoperfused lesions in patients with acute ischemic stroke (AIS). However, the precise delineation of penumbra in ASL is still challenging. Goal(s): To develop a deep learning (DL) model based on ASL to identify eligible candidates for endovascular treatment in AIS patients. Approach: A multi-task DL model was proposed for simultaneous segmentations of penumbra and infarct by combining cerebral blood flow and DWI images. Results: The multi-task segmentation performed well, which is comparable to the results achieved by radiologists. Impact: The proposed approach performed well for the segmentation of penumbra and infarct, which could provide a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.