  A stroke occurs when an artery in the brain ruptures and bleeds or when the
blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's
tissues due to the rupture or obstruction resulting in tissue death. The Middle
cerebral artery (MCA) is the largest cerebral artery and the most commonly
damaged vessel in stroke. The quick onset of a focused neurological deficit
caused by interruption of blood flow in the territory supplied by the MCA is
known as an MCA stroke. Alberta stroke programme early CT score (ASPECTS) is
used to estimate the extent of early ischemic changes in patients with MCA
stroke. This study proposes a deep learning-based method to score the CT scan
for ASPECTS. Our work has three highlights. First, we propose a novel method
for medical image segmentation for stroke detection. Second, we show the
effectiveness of AI solution for fully-automated ASPECT scoring with reduced
diagnosis time for a given non-contrast CT (NCCT) Scan. Our algorithms show a
dice similarity coefficient of 0.64 for the MCA anatomy segmentation and 0.72
for the infarcts segmentation. Lastly, we show that our model's performance is
inline with inter-reader variability between radiologists.
