  The hyperdense middle cerebral artery (MCA) dot sign has been reported as an
important factor in the diagnosis of acute ischemic stroke due to large vessel
occlusion. Interpreting the initial CT brain scan in these patients requires
high level of expertise, and has high inter-observer variability. An automated
computerized interpretation of the urgent CT brain image, with an emphasis to
pick up early signs of ischemic stroke will facilitate early patient diagnosis,
triage, and shorten the door-to-revascularization time for these group of
patients. In this paper, we present an automated detection method of segmenting
the MCA dot sign on non-contrast CT brain image scans based on powerful deep
learning technique.
